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02. Eye Screening Support System

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Digital Eye Screening Support System
Final Project report
Submitted to the school of Biomedical Engineering for the partial fulfillment of
requirements for the degree of Bachelor of science in Biomedical Engineering
Jimma Institute of Technology (JIT)
Jimma University
Jimma, Ethiopia
Team members
Name
1.
2.
3.
4.
5.
6.
ID No
Ajaeba Mustefa…………….0137/07
Bisrat Amanuel…………….0430/07
Fikeremariam Getachew….. 0708/07
Lemlem Solomon…………. 1070/07
Lemlem Tiruneh…………... 1073/07
Mekdes Endashaw………....04776/06
Advisors
Gizeaddis Lamesgin (Ph.D.)
Melese Umma (MSc.)
June 13, 2019
Digital Eye Screening Support System 2019
Executive summary
Human eye is a vital organ of vision which reacts to light and pressure. It has a number of
components which include but are not limited to the cornea, iris, sclera, pupil, lens, retina, optic
nerve and vitreous. Retina is essential component of the eye and serves the primary purpose of
photoreception. The center of the retina is the optical disc, a circular to oval white area measuring
about 3 mm (about 1/30 of retina area). There are various things that affect structure of the eye,
causing visual impairment and blindness, such as Glaucoma, Cataract, Age related macular
degeneration, retinopathy etc.
In Ethiopia eye diseases and blindness occurs especially for elder people. From several type of eye
disease glaucoma, ARMD and cataract are common. These diseases can be prevented or treated if
it caught earlier. But in Ethiopia, due to lack of awareness, less organized hospitals diagnosis
system, and lack of devices in low level health facilities there is no habit to do early checkup before
it aggravates. Diseases like glaucoma and retinopathy do not show symptom in early stage and
they are irreversible if they already damage the eye. Ophthalmoscopy, slit-lamp microscopy,
Gonioscopy and manual optical devices are few technologies that are used to diagnose/screen the
eye. The quality of images acquired using optical imaging modalities in developing countries
including Ethiopia is very less. High-tech imaging methods like funds-camera are not available in
most health facilities in the country.
In this project, an eye screening support system that is user friendly, cost effective, and portable is
proposed. The system includes image acquisition, enhancement, segmentation and cup to disc ratio
calculation. Images acquired can also be saved digitally for later use. In addition, pressure of the
cornea is measured using an IR sensor.
The developed system will have a great impact in supporting physicians in their decision in the
diagnosis of the eye.
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Digital Eye Screening Support System 2019
Disclosure statement
We hereby declare that this report, submitted to school of Biomedical Engineering at the Jimma
Institute of Technology as a partial fulfillment of the intervention and evaluation report guideline
requirements for the degree of Bachelor of Science in Biomedical Engineering, has not been
submitted as an exercise for a degree at any other university. We also certify that the work
described herein is entirely our own work with the exception of paraphrased or quoted work whose
sources are appropriately cited in the references. This report may be made available within the
university library and may be Photocopied or loaned to other libraries as a reference for other
work.
Students Name
Signature
1. Ajaeba Mustefa
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2. Bisrat Amanuel
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3. Fikeremariam Getachew
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4. Lemlem Solomon
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5. Lemlem Tiruneh
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6. Mekdes Endashaw
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Name of Advisor
Signature
Date
Date
1. Gizeaddis Lamesgin (PhD)
----------------------
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2. Melese Umma (MSc)
----------------------
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Name of Evaluator
Signature
Date
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Digital Eye Screening Support System 2019
Statement of approval
On behalf of the school of Biomedical Engineering at the Jimma Institute of Technology, we the
mentor(s) of this project, digital eye screening support system and I, the evaluator, confirm that
this project was approved as the topic for the intervention and evaluation project for the students,
Ajaeba Mustefa, Bisrat Amanuel, Fikremariam Getachew, Lemlem Solomon, Lemlem Tiruneh
and Mekdes Endashaw.
Mentor name
signature
date
signature
date
1. Gizeaddis Lamesgin (PhD)
2. Melese Umma (MSc)
Evaluator name
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Digital Eye Screening Support System 2019
Acknowledgement
First of all, we would like to give our deepest thanks to our Almighty God. Next our deepest
gratitude is also going to Jimma University Institute of Technology (JIT) and School of Biomedical
Engineering (BME) for preparing work area and materials for implementation. Then we would
like to thank our instructor Dr. Timothy Kwa for giving us his advises. we heart fully thank our
clients Dr. Tizazu Sisay, DR. Dagmawit, Dr Tarekegn from ophthalmology department in Jimma
Medical Center We also give special thanks for our advisors Gizeaddis Lamesgin (PhD) and M/r
Melesse Umma (MSc). Our department head Engineer Wasihun Alemayehu (MSc) and instructor
Shimelis Nigussie (BSc) also give as their advice and we are very grateful for their contribution.
We also give our enormous thanks to Abel Worku (Msc) and Habtamu Abafoge for their
unforgettable contributions. We are grateful for BME Lab assistants and other willing people for
the interview.
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Digital Eye Screening Support System 2019
Table of Contents
Executive summary ........................................................................................................................ i
Disclosure statement ..................................................................................................................... ii
Statement of approval.................................................................................................................. iii
Acknowledgement ........................................................................................................................ iv
List of Figure ................................................................................................................................ ix
List of Table ................................................................................................................................... x
Acronym........................................................................................................................................ xi
CHAPTER ONE ........................................................................................................................... 1
INTRODUCTION ....................................................................................................................... 1
1.1.
Clinical background ......................................................................................................... 1
1.1.1 Glaucoma ........................................................................................................................ 1
1.1.2. Cataract .......................................................................................................................... 2
1.1.3. Age-related macular degeneration ................................................................................. 2
1.1.4. Diabetic retinopathy ...................................................................................................... 3
1.2.
Literature review .............................................................................................................. 4
1.3.
Existing solutions ............................................................................................................. 5
1.3.1.
Perimeter ................................................................................................................... 5
1.3.2.
Ophthalmoscope ....................................................................................................... 6
1.3.3.
Gonioscopy ............................................................................................................... 6
1.3.4.
Pachymetery.............................................................................................................. 6
1.3.5.
Optical Coherence Tomography ............................................................................... 6
1.4.
Gap analysis ..................................................................................................................... 7
1.4.1.
Subjective decision ................................................................................................... 7
1.4.2.
Accessibility.............................................................................................................. 7
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Digital Eye Screening Support System 2019
1.4.3.
Cost ........................................................................................................................... 7
1.4.4.
Portability.................................................................................................................. 7
1.4.5.
Manual diagnosis ...................................................................................................... 8
1.4.6.
Time .......................................................................................................................... 8
1.5.
Problem statement ............................................................................................................ 8
1.6.
Objective of the project and constraints ........................................................................... 9
1.6.1.
General Objective ..................................................................................................... 9
1.6.2.
Specific Objectives ................................................................................................... 9
1.6.3.
Constraints ................................................................................................................ 9
1.7.
Significance of the study .................................................................................................. 9
1.7.1.
Store images .............................................................................................................. 9
1.7.2.
Location independent .............................................................................................. 10
1.7.3.
Time saving ............................................................................................................. 10
1.7.4.
Reliable result ......................................................................................................... 10
1.7.5.
User friendly ........................................................................................................... 10
1.8.
Scope of the study .......................................................................................................... 10
1.9.
Breakthrough .................................................................................................................. 11
CHAPTER TWO ........................................................................................................................ 12
DESIGN STRATEGY .............................................................................................................. 12
2.1.
Background information ................................................................................................ 12
2.2.
Evaluation of brainstormed idea .................................................................................... 12
2.2.1 Wearable and wireless intraocular pressure measurement ........................................... 12
2.2.2 Image acquisition, enhancement and segmentation system for glaucoma
detection .................................................................................................................................... 14
2.2.3 Integrating the slit lamp (bio microscope) with computer application ......................... 15
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Digital Eye Screening Support System 2019
2.3.
How the matrix is obtained?........................................................................................... 16
2.4.
Proposed initial design ................................................................................................... 18
2.3.1. Image acquisition ........................................................................................................ 19
2.3.2. Image Enhancement .................................................................................................... 19
2.3.3. Edge detection ............................................................................................................. 19
2.3.4. Image segmentation ..................................................................................................... 20
2.3.5.
Cup-to-disc ratio ..................................................................................................... 20
2.3.6.
IOP measurement .................................................................................................... 21
2.4.
Components lists ............................................................................................................ 21
2.4.1.
Hardware part.......................................................................................................... 21
2.4.2.
Software part ........................................................................................................... 24
2.5.
Implementation plan ....................................................................................................... 24
CHAPTER THREE .................................................................................................................... 25
FINAL DESIGN ....................................................................................................................... 25
3.1. Steps for construction of prototype ................................................................................... 25
3.3. Final product ...................................................................................................................... 27
3.4. Final components list ......................................................................................................... 27
3.5. Graphical user interface ..................................................................................................... 28
3.6.
Simulation and results .................................................................................................... 33
CHAPTER FOUR ....................................................................................................................... 35
TESTING AND RESULTS ...................................................................................................... 35
4.1. Design criteria .................................................................................................................... 35
4.1.1. Accuracy ...................................................................................................................... 35
4.1.2. Cost ............................................................................................................................. 35
4.1.3. Portability ................................................................................................................... 35
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Digital Eye Screening Support System 2019
4.1.4. Easy to use ................................................................................................................... 35
4.1.5. Sensitivity .................................................................................................................... 36
4.1.6. Time saving ................................................................................................................. 36
4.2. Testing plan ........................................................................................................................ 36
4.2.1. Market Testing ............................................................................................................. 36
4.2.3. Sensitivity test.............................................................................................................. 36
4.3. Testing conducted .............................................................................................................. 37
CHAPTER FIVE ........................................................................................................................ 38
CONCLUSION AND RECOMMENDATION ........................................................................ 38
5.1. Conclusion.......................................................................................................................... 38
5.2. Recommendations .............................................................................................................. 39
References ................................................................................................................................................ 40
Appendices ............................................................................................................................................... 43
Appendix A ............................................................................................................................... 43
Pair wise comparison chart .................................................................................................... 43
Appendix B ............................................................................................................................... 44
Sharp IR sensor datasheet ...................................................................................................... 44
Appendix C ............................................................................................................................... 45
Camera specification ............................................................................................................. 45
Appendix D ............................................................................................................................... 46
Arduino code ......................................................................................................................... 46
Appendix E................................................................................................................................ 48
MATLAB code ...................................................................................................................... 48
Appendix F ................................................................................................................................ 58
Statement of approval from ophthalmology department ....................................................... 58
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Digital Eye Screening Support System 2019
List of Figure
Figure 1: different types of glaucoma indications .......................................................................... 4
Figure 2: block diagram of IOP measurement .............................................................................. 13
Figure 3: block diagram of image processing method .................................................................. 14
Figure 4: block diagram of slit lamp and computer integration for analysis ................................ 15
Figure 5: algorithm or flow chart .................................................................................................. 18
Figure 6: image of normal eye and glaucoma eye ........................................................................ 19
Figure 7: volk 90D ........................................................................................................................ 22
Figure 8: CCD ............................................................................................................................... 22
Figure 9: Arduino .......................................................................................................................... 22
Figure 10: LCD ............................................................................................................................. 23
Figure 11: Sharp IR sensor ........................................................................................................... 23
Figure 12: potentiometer ............................................................................................................... 24
Figure 13: prototype construction: A) shows IOP design on Proteus B) Prototype construction of
image C) graphical user interface D) final simulation Part .......................................................... 26
Figure 14: final prototype ............................................................................................................. 27
Figure 15: graphical user interface image ..................................................................................... 28
Figure 16:image acquisition in GUI ............................................................................................. 29
Figure 17: Image enhancement on GUI ........................................................................................ 30
Figure 18: edge detection on GUI ................................................................................................. 31
Figure 19: image segmentation on GUI ........................................................................................ 32
Figure 20: cup to disc ratio on GUI .............................................................................................. 33
Figure 21: A) Proteus results for normal B) IOP Proteus results for high IOP ............................ 34
Figure 23: Sharp IR sensor Datasheet A ....................................................................................... 44
Figure 24:Sharp IR sensor Datasheet B ........................................................................................ 44
Figure 25: camera specification .................................................................................................... 45
Figure 26: statement of approval from JUSH ophthalmology ward ............................................. 58
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Digital Eye Screening Support System 2019
List of Table
Table 1: screening Pugh matrix .................................................................................................... 16
Table 2: scoring Pugh matrix ........................................................................................................ 17
Table 3: Iteration of the device ..................................................................................................... 26
Table 4: testing plan and result ..................................................................................................... 37
Table 5: pair wise comparison chart ............................................................................................. 43
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Digital Eye Screening Support System 2019
Acronym
1. ARMD………………………….... Age related macular degeneration
2. CCD………………………………. Charged capacitive device
3. CCT………………………………..Central corneal thickness
4. CDR………………………………. Cup to disc ratio
5. FT…………………………………. Fourier transform
6. GUI………………………………. Graphic user interface
7. HRT……………………………….. Heidelberg retinal tomography
8. IOP……………………………….. Intraocular pressure
9. IR………………………………….. Infrared
10. LCD……………………………….. Liquid crystal display
11. MATLAB………………………….. Matrix Laboratory
12. OAG……………………………. .. Open angle glaucoma
13. OCT……………………………….. Optical coherence tomography
14. PSD…………………………………Positive sensitive detector
15. ROI………………………………. Region of interest
16. RPE………………………………. Retinal pigment epithelium
17. USB……………………………….. Universal serial bus
18. WHO……………………………… World health organization
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Digital Eye Screening Support System 2019
CHAPTER ONE
INTRODUCTION
1.1.
Clinical background
Human eye is a vital organ of vision which reacts to light and pressure. It has a number of
components which include but are not limited to the cornea, iris, sclera, pupil, lens, retina,
optic nerve and vitreous [1]. The protective outer layer of the eye is called the sclera. Retina
is essential component of the eye and serves the primary purpose of photoreception [2]. Retina
is approximately 0.5mm thick and covers the inner side at the back of the eye. The center of
the retina is the optical disc, a circular to oval white area measuring about 3 mm (about 1/30
of retina area). The mean diameter of the vessels is about 250μm [3]. All other structures of
the eyes are subsidiary and act to focus images on the retina, to regulate the amount of light
the eye or to provide nutrition, protection or motion [3]. The eye is often compared to a camera.
Each gathers light and then transforms that light into pictures. Extraocular muscles help move
the eye in different directions. Nerve signals that contain visual information are transmitted
through the optic nerve to the brain [4]. The eye plays a very important role not only in life but
also in human body. Also it gives us the sense of sight, allow us to see and interpret colors,
dimension of object [5]. There are various things that affect structure of the eye, cause visual
impairment and blindness. The main visual disorders are listed below.
1.1.1 Glaucoma
Glaucoma is an acquired disease of the optic nerve (neuropathy) characterized by specific
changes of the optic nerve and by visual field defects that correspond to the areas of optic nerve
structural damage. It is one of the many eye diseases can lead to the blindness if it is not
detected and treated in proper time [6]. It is often associated with the increased in the
intraocular pressure (IOP) of the fluid known as aqueous humor in eye. High eye pressure, or
intraocular pressure, is often present and is the only modifiable risk factor for glaucoma [7].
Open-angle glaucoma (OAG) is the most common form of glaucoma. It occurs when the fluid
in the anterior chamber reaches the angle between the cornea and iris and passes too slowly
through the meshwork drain. As the fluid builds up, the pressure inside the eye rises to a level
that may damage the optic nerve [7].
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Digital Eye Screening Support System 2019
Glaucoma has been shown by numerous epidemiological studies to be a leading cause of
blindness worldwide, ranging from 1% - 2% of the population >40 years of age among various
regions. Of those with glaucoma, primary open-angle glaucoma is generally the most prevalent
type. It is found to be one of the blinding eye diseases, causing 62,000 blind people in Ethiopia
according a national survey in 2006 [8]. Glaucoma is detected through a comprehensive eye
examination by an eye care professional (ECP).
1.1.2. Cataract
Cataracts are a clouding of the lens, resulting in a blurred image. The lens is made from water
and protein which arranged in a way that keeps the lens clear and allows light to pass through
[9]. Oxidative damage caused by free radicals is considered to be an important factor in aging
and the development of chronic diseases, including cataract formation [10] . As age increases,
some of the protein may clump together and start to cloud a small area of the lens, resulting in
a cataract that reduces the amount of light reaching the retina. Over time, the cataract may
slowly grow larger and cloud more of the lens, making vision gradually duller or more blurred
[9]. Severe cataracts are a major cause of treatable blindness throughout the world [10].
There are three main types of cataract:
•
Nuclear cataracts: are the most common type and named because they occur in the
center of the lens
•
Cortical cataracts: it starts in the cortex (periphery) of the lens and gradually extend to
the center of the lens
•
Subcapsular cataracts: are those in which the opacities are concentrated beneath or
within the capsule of the lens.
Cataract development is part of the ageing process. Hence, although lifestyle changes may
reduce the rate of development for some people, cataracts cannot be prevented. However,
surgery can correct the condition [9].
1.1.3. Age-related macular degeneration
In Age-related macular degeneration (ARMD), clumps of yellowish material known as drusen
gradually accumulate within and beneath the retinal pigmented epithelium (RPE). The RPE
cells may die and therefore no longer support the photoreceptors, which then cannot function,
resulting in loss of vision in that part of the retina [10]. If the photoreceptors in the macula are
affected, this can seriously impair fine visual skills; this situation is sometimes referred to as
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Digital Eye Screening Support System 2019
‘dry’ macular degeneration (or central, geographic atrophy), and is the most common form of
ARMD [11]. The prevalence (for all degrees from minor to severe) of ARMD rises from
approximately 2% in the 54-64 years old age group to 30% in the 75-85 years old age group
[11].
1.1.4. Diabetic retinopathy
Diabetic retinopathy is a common complication of diabetes. Poor glucose control during
diabetes affects the tiny blood vessels of the retina. The arteries become weakened and leak
blood, leading to swelling or edema in the retina and decreased vision [12]. As diabetes
progresses, circulation problems can also deprive the retina of oxygen. If abnormal blood
vessel growth continues, scar tissue formation may cause retinal detachment and glaucoma
may also develop [9]. There are multiple tests available for diagnosis and detection of eye diseases
A. Ophthalmoscopy
This diagnostic procedure helps the doctor to view the interior of the eye especially the retina.
Eye drops are used to dilate the pupil and then examine the shape and color of the optic nerve
by using small device with a light on the end to light and magnify the optic nerve. If the intra
ocular pressure is not within the normal range or if the optic nerve looks unusual, it is necessary
to take the exam many times [8].
B. Slit-Lamp Microscopy
Slit-lamp microscopy is of fundamental importance in the treatment of eye disease like
glaucoma. In this examination, the conjunctivae, anterior chamber, eye iris, lens, etc., are
observed, but an auxiliary lens may also be used in combination in order to observe the anterior
chamber angle and ocular fundus [13].
C. Gonioscopy
Gonioscopy is a method of evaluating the anterior chamber angle to provide information
regarding the type of glaucoma. It can also be utilized therapeutically for procedures such as
laser trabeculoplasty and goniotomy. It is indispensable in the treatment and diagnosis of
glaucoma. In Gonioscopy, it is important to properly recognize the various structures
composing the anterior chamber angle, such as Schwalbe's line, the trabecular meshwork, the
scleral spur, and the ciliary band [14].
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D. Perimeter
Perimetry is a visual field test that produces a map of a complete field of vision. The normal
visual field has an elongated elliptical shape, and with respect to the fixation point, it measures
60 degrees superiorly and medially, 70-75 degrees inferiorly, and 100-110 degrees temporally.
This test determines whether the vision is affected. The two means for measuring the visual
field are dynamic and static measurement. Perimeters express the brightness of the target in
units of apostilbs (asb) [3]. Figure 1 shows the different types of glaucoma indications.
Figure 1: different types of glaucoma indications
1.2.
Literature review
According to K. Nirmala et.al in Analysis of CDR Detection for Glaucoma Diagnosis to
segment the color fundus camera image and calculate the features to segment optic disc and
cup separately. The study indicates that according to WHO, Glaucoma is the second leading
cause of blindness that contributes to approximately 5.2 million cases of blindness (15% of
total blindness. The objective of this study is to detect Glaucoma using K-Means clustering
segmentation and Gabor Wavelet Transform of fundus image to obtain the accurate boundary
delineation. In order to extract the optic disc and cup, a region of interest around the optic cup
and disc must first be delineated. The disc and cup extraction can be performed in the entire
image localizing the ROI (region of interest) would help to reduce the computational cost as
well as improve the segmentation accuracy.
Article by Akira Sawada et.al on Vertical cup-to-disc ratio measurement for diagnosis of
glaucoma on fundus images mainly focus on analyzing the optic disc on a retinal fundus image,
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Digital Eye Screening Support System 2019
which is important for diagnosis of glaucoma. According to this article the vertical C/D ratio
is most important factor for diagnosis of glaucoma, because the contrast of the cup region and
the rim one was high. Thus, it is needed to measure C/D ratio automatically. Here, glaucoma
cases tend to have enlarged cup regions as against the normal cases. The method used in this
article is achieved a concordance rate of 86%, extraction rate of 96%, and over-extraction rate
of 11% relative to the disc areas determined by an ophthalmologist. In this study, we also
presented a method for recognizing glaucoma by calculating C/D ratio. The method correctly
identified 80% of glaucoma cases and 85% of normal cases. Although the proposed method is
not error-free, the results indicated that it can be useful for the analysis of the optic disc in
glaucoma examinations.
As JG Hillman et.al stated on central corneal thickness and optic disc relationship in an elderly
population, CCT has been shown to be a powerful risk factor for the progression of ocular
hypertension and preperimetric glaucomatous optic neuropathy to primary open angle
glaucoma. In this study approximately 1500 subjects are participated with aim of determining
population based normative CCT data for elderly subjects with and without diabetes, and to
find the relationship between CCT and IOP. Both CCT and optic disc was measured by
ultrasound pachymetry and morphometrically stated using Heidelberg retinal tomography
(HRT2). This study show CCT is positively related to IOP with thinner corneas requiring less
force than expected to achieve applanation by Goldmann applanation Tonometry and diabetic
patients had significantly greater CCTs than non-diabetic patients.
1.3.
Existing solutions
Different technical methods are used for diagnosis of eye disease.
1.3.1. Perimeter
Perimetry is a visual field test that produces a map of a complete field of vision. The normal
visual field has an elongated elliptical shape, and with respect to the fixation point, it measures
60 degrees superiorly and medially, 70-75 degrees inferiorly, and 100-110 degrees temporally.
This test determines whether the vision is affected. The two means for measuring the visual
field are dynamic and static measurement. Perimeters express the brightness of the target in
units of apostilbs (asb) [3].
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1.3.2. Ophthalmoscope
It is an instrument for inspecting the interior of the eye. The device devise consists of a strong
light that can be directed in to the eye by small mirror or prism. The light reflects of the retina
and back through small hole in the ophthalmoscope, through which the examiner sees a nonstereoscopic magnified image of the structures at the back of the eye, including the optic disc,
retina, retinal blood vessels, macula and choroid. It is particularly useful as a screening tool for
various ocular diseases. Such as: diabetic retinopathy [3].
1.3.3. Gonioscopy
This diagnostic exam helps determine whether the angle where the iris meets the cornea is
open and wide or narrow and closed. During the exam, eye drop is used to numb the eye. A
hand-held contact lens is gently placed on the eye. This contact lens has a mirror that shows if
the angle between the iris and cornea is closed and blocked or open and wide [14]. Gonioscopy
is indispensable in the treatment and diagnosis of glaucoma. In Gonioscopy, it is important to
properly recognize the various structures composing the anterior chamber angle, such as
Schwalbe's line, the trabecular meshwork, the scleral spur, and the ciliary band [15].
1.3.4. Pachymetery
Pachymetery is a simple painless test to measure the thickness of cornea which is the clear
window at the front of the eye. A probe called Pachymetery is gently placed on the front of the
eye (cornea) to measure thickness. It helps to diagnosis, because corneal thickness has a
potential to influence eye pressure reading [16].
1.3.5. Optical Coherence Tomography
Optical coherence tomography (OCT) is an imaging modality that performs high resolution,
micrometer-scale cross sections of the eye and other biological structures. This technology
presents a clearer picture of the pathophysiology of disease. Also, OCT is useful in determining
response to therapy [17].
1.3.6. Manual screening using 3D lenses
Manual screening system is a technique used by the physicians with the help of 3D lens to
make diagnosis on the slit lamp or fundus copy. To watch the optic nerve 3D lens is needed.
They also analyze the cup to disc ratio manually through experience they guess the diameter
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and give the ratio. If the cup is large they estimate that, the patient may have glaucoma. They
also relate the result with IOP.
1.4.
Gap analysis
The above existing solutions have their own gaps. From those;
•
Manual observation/eye screening
•
Requires subjective decision making
•
Most of them are unavailable in health centers of Ethiopia
•
They are expensive
•
Not portable
•
There is delay on diagnosis
1.4.1. Subjective decision
In the current technologies, diagnosis and analysis is made by the examiner making the
decision subjective. This subjective decision making process is prone to error leading to
misdiagnosis.
1.4.2. Accessibility
Currently, the devices are less accessible in Ethiopia, like Tonometry, Ophthalmoscopy which
is mostly used for diagnosing purpose. They are found in some hospital in limited amount of
number. These devices are not found at health centers and in rural areas. This inaccessibility
of device leads many peoples to not diagnosed early and become blind.
1.4.3. Cost
The costs of devices which are currently used are expensive. To diagnose glaucoma there is a
need of different tests. These tests need screening devices. In Jimma medical center there is
tonometry device used for pressure measurement and slit lamp microscope used to see the
status of the eye and analyze the stage of glaucoma. Although, the devices are useful, there is
limited number of those devices due to cost.
1.4.4. Portability
The devices are large in size table top devices. The physician and patient must go to the device
to diagnose since; the device is not portable. For diagnosis, integrating different devices and
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Digital Eye Screening Support System 2019
correlate their output is needed, which is very tiresome for patients. In glaucoma most of
patients are elders, for them to diagnose in many different wards is very difficult.
1.4.5. Manual diagnosis
Currently in Ethiopia, physician most commonly use slit lamp with lens to observe and screen
eye looking for abnormalities like retinopathy, glaucoma, neuropathy etc. Analysis of images
are done manually and there is no means of storing the images except when using sophisticated
and expensive devices like funds camera. Manual diagnosis is subjective which may lead to
false estimation.
1.4.6. Time
Manual observation of the eye looking for abnormalities is a time consuming procedure.
Absence of digital storage system when using traditional screening methods is also another
cumbersome. If a physician is unsure with the first decision, he may redo the observation
because of lack of a stored image. This increases patient’s waiting time.
1.5.
Problem statement
Eye disease can be treated if it is caught earlier. Glaucoma, cataract, retinopathy and ARMD
are the most occurring eye diseases in Ethiopia. Now days, Glaucoma becomes familiar in
Ethiopia. Many people are losing their sights because of glaucoma, especially people who aged
above 40 years. These problems come due to lacking habit of early checkup for eyes,
unavailability of suitable detection or screening mechanisms in low level health facilities and
lack of awareness, even among some health professionals in Ethiopia awareness and
understanding of glaucoma is low. The worst scenario is that glaucoma is a chronic disease
characterized by irreversible optic nerve damage and visual field loss. Most people go to
hospital after the disease aggravates. After advanced stage of glaucoma, the treatment become
only medication and surgery to preserve the rest undamaged optic nerve. Thereby, if one come
and early diagnosed, his/her sight can be preserved. Currently in Ethiopia, manual eye
screening procedure is practiced to diagnose the eye. This method is subjective and prone to
error. High-tech devices are not accessible in most health facilities because of their expensive
cost. In this project, simple, cost effective, accessible and suitable eye screening support system
is proposed.
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1.6.
Objective of the project and constraints
1.6.1. General Objective
The general objective of this project is to develop a digital eye screening support system.
1.6.2. Specific Objectives
The specific objectives of this project are:
•
To digitalize the eye screening system.
•
To enhance retinal images and segment the optical disk
•
To provide cost effective, portable and easy to use eye screening system
•
To help physicians analyze presence of glaucoma and other disease diseases.
•
To simplify the work load of physicians.
1.6.3. Constraints
Constrains of the project are:
•
Portability: small size
•
Sensitivity: sensitive to detect small amount of high pressure.
•
Cost effective: the cost is expected to be < 5000birr
•
Easy to use: it should not take more than 30 min training to understand
•
Feasibility: components which are needed to produce are available.
•
Accessibility: can be accessible in any health organization.
1.7.
Significance of the study
This project has a great significance in glaucoma disease detection. It has also other
significances.
1.7.1. Store images
This project enables the physician to store patient information and diagnosis result. It helps for
further analysis of image when it is needed. It also helps to simplify follow up since, the
physician can see the previous result when the patient comes again.
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Digital Eye Screening Support System 2019
1.7.2. Location independent
Since, the image acquiring and storing in computer is possible, it allows the clinicians to review
same data at the same time even though, they are found in different locations. This enables
health center health officers to ask assistance and further analysis from residents or specialists.
1.7.3. Time saving
The current technology does not provide analyzation rather only input image. Analyzation is
the physicians work. Therefore, watching the image and analyze it takes some period of time.
This project provides simple and fast analyzer system which, used to decrease waiting time
and suffering due to long waiting time. Many elders should have been diagnosed without any
long bother.
1.7.4. Reliable result
The current technology does not capture image and do analysis. The physician watches directly
in to eye through slit lamp and analyze the coming image by him/her self. This result unreliable
result sometimes and when the physician is in rush he/she may not see detail of the eye and
forget some features. However, this project gives quality image that passes under image several
image processing steps. If the ophthalmologist needs to see extracted image, he can see it easily
by pressing the graphical interfacing module. Example, optic nerve can be seen by this
technology.
1.7.5. User friendly
This system is easily understandable by the user since, there is no complexity in the system.
The device will finish almost more than 50% of physician work. Therefore, the physician will
have satisfaction and dedication to his/ her work. It also provides comfortable diagnosing
system for both the user and the patient. The user can sit without bending towards the slit lamp
and the patient can even sleep or be in his supine position. Thereby, it is comfortable and user
friend system.
1.8.
Scope of the study
This system mainly focused on elder people or people who aged greater than 40 years. But
glaucoma sometimes happens in children also.
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Digital Eye Screening Support System 2019
1.9.
Breakthrough
When we went to the hospital, we wonder to visit the ophthalmology department. Because, we
heard about glaucoma and many other eye diseases. While visiting, we saw an ophthalmologist
who were diagnosing a patient eye through slit lamp. He was stopped, and he took many time
to diagnose one person. We asked him and he told us, that he works this for 5 years and many
times a day. This will cause him back pain and also other eye problems. Therefore, we inspired
to design eye screening support system which give comfort to physician with processed image
and IOP integration. This method is also good for the patient also.
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Digital Eye Screening Support System 2019
CHAPTER TWO
DESIGN STRATEGY
2.1.
Background information
The leading causes of visual impairment are primarily age-related eye diseases including
cataracts, diabetic retinopathy, glaucoma, and age-related macular degeneration. Glaucoma
is an acquired disease of the optic nerve (neuropathy) characterized by specific changes of
the optic nerve and by visual field defects that correspond to the areas of optic nerve
structural damage. Without showing any symptom early stage glaucoma will damage optic
nerve. Mostly patients don’t know the disease until it reaches the advanced stage. The
damage caused by glaucoma is irreversible, but if it detected earlier the treatment can
prevent its progression. Thus, early detection of glaucoma is essential.
The goal of this project is to provide an accurate, low cost, easy to use, fast and portable
eye disease screening device based on intraocular pressure estimation and image
processing with the aim of decreasing blindness due to different eye disease. This device
is mainly used in government or private hospital, health sector and clinics. In order to do
these different alternatives was provided such as Wearable and wireless intraocular
pressure measurement, Integrating the slit lamp (bio microscope) with computer
application, Image acquisition, enhancement and extraction system for eye disease
detection.
2.2.
Evaluation of brainstormed idea
1. Wearable and wireless intraocular pressure measurement
2. Image acquisition, enhancement and extraction system
3. Integrating the slit lamp (bio microscope) with computer application
2.2.1 Wearable and wireless intraocular pressure measurement
Intraocular pressure is a fluid pressure in the eye. It is an important aspect in the evaluation
of patient at a risk of glaucoma. Eye pressure is measured in mmHg. Normal eye pressure
ranges from 12-22 mmHg, and eye pressure of greater than 22 mmHg is considered higher
than normal. Although the person does not show sign of glaucoma, this is referred to that
ocular hypertension. This possible solution measures the eye pressure and suspect
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Digital Eye Screening Support System 2019
probability glaucoma. This method is done by, attach the sensor at eye glass. It acquires the
pressure difference at the iris and measure the distance between iris and cornea. These inputs will
inter to the processing unit and analyzed. On the LCD length between iris and cornea, the eye
pressure and comments will be displayed as an output. Figure shows the block diagram of this
technique
Wear the
eye glass
Receive
signal
wirelessly &
ADC
Acquiring
parameters
Out put
Cornea length
Eye pressure
Figure 2: block diagram of IOP measurement
Strength
•
Since it is non- contact with eye, its comfortable
•
Safe
•
Easy to use by everyone
Weakness
The main problem of this device is its accuracy. It measures the eye pressure only which is one
of the risk factor of glaucoma and sometimes in normal eye pressure also glaucoma may
happen.
Required Materials
•
Pressure sensor
•
Eye glass
•
Distance sensor
•
Analogue to digital convertor
•
LCD
•
Electrical components
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Digital Eye Screening Support System 2019
2.2.2 Image acquisition, enhancement and segmentation system for glaucoma
detection
In this method, a 3D lense will be attached with a mini camera to acquire high-quality images of
the eye. The acquired images will be enhanced, segmented and cup to disc ratio is calculated to
diagonise glacoma. In addition pressure sensor will also be incorporated to acquire intra-ocular
presure. Graphical user interface (GUI) will be develped to acquire image and pressure signal,
enhance and exract the image. The system will be developed using MATLAB. The result will be
displayed on the computer.
GUI
Camera with
lens
Image
acquisition
Processing
unit
Image
enhancement
Pressure
sensor
Image
segmentation
Result
Image
CDR
LCD
Pressure
value
Figure 3: block diagram of image processing method
Strength
•
Accurate, portable and easy to use
•
Cost effective
•
Save image and personal information is possible
•
Can diagnose other eye disease like retinopathy, degenerative eye diseases etc.
•
Improve the quality of diagnosis
•
Used in health center
Weakness
•
Do not analyze image
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Digital Eye Screening Support System 2019
•
Self-diagnose not possible
Required Materials
•
Camera
•
3D lens
•
Raspberry pi 3
•
Pressure sensor
•
Touch screen LCD
•
Electrical components
2.2.3 Integrating the slit lamp (bio microscope) with computer application
This method is done by acquiring image from the slit lamp (bio microscope) and enhances that
image and display in the computer. Therefore, the patient sits on the slit lamp instrument and the
physician can analyze anywhere he want.
Camera
attached to
slit lamp
Image
acquisition
from slit
lamp
Computer application
Image
processing
Processed
image
displayed
Computer
Figure 4: block diagram of
slit lampapplication
and computer integration for analysis
Strength
This method will:
Computer application
•
Decrease the physician load
•
Increase time
•
Increase quality of diagnosis
•
prevents the physician from back pain and sight problem
Computer application
Weakness
•
Cost
•
Need slit lamp device
•
Need ophthalmologist
Computer application
Computer application
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Computer application
Digital Eye Screening Support System 2019
Required Materials
•
Camera
•
Slit lamp
•
PC app
From the above possible solutions image acquisition, enhancement and extraction system for
glaucoma detection method is selected using Pugh Matrix.
Pugh matrix for screening proposed concept
Evaluation criteria
Wireless
IOP Image processing
measurement
Integrating slit lamp
with
comp
application
Accuracy
0
1
1
Cost effectiveness
1
0
1
Portability
1
1
1
Accessibility
1
1
-1
Easy to use
0
1
0
Feasibility
-1
1
1
Sensitivity
0
1
0
Total
0 = 3, -1 = 1, +1 = 3
0 = 1, -1 = 0, +1 = 6
0 = 2, -1 = 1, +1 = 4
Rank
3
1
2
Continue…???
No
Yes
Revised
Table 1: screening Pugh matrix
2.3.
How the matrix is obtained?
Our baseline is the system we have in place at the moment, so we score this a null (zero) against
our criteria. It is used as a reference. Now consider option 1. In relation to criteria 1 (Accuracy),
do we consider that it is better, the same as, or worse than the baseline? If it's better, we give it a
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Digital Eye Screening Support System 2019
+; if it's the same, we give it a 0; and if it's worse, we give it a -. As we can simply understand
from the second design which is image processing, it is accurate. Therefore, it is better, and we
gave a value of +. In terms of criteria 2(cost), it's worse than the baseline. For criteria 3(portability),
4(Accessibility), 5(easy to use) and criteria 6 it is better for the last criteria (sensitivity), it is better.
Thus the three designs are screened. Next, we multiply all values in the above table by the
corresponding weight.
Design criteria
weight (priority)
Accuracy
0.29
Cost effective
0.24
Portability
0.19
Accessibility
0.14
Easy to use
0.07
Feasibility
0.063
Sensitivity
0.01
Pugh matrix for concept screening
Feature
Pressure
Image
Bio microscope with
measurement
enhancement
computer app
0.29
1
0.29
3
0.87
2
0.58
Cost effectiveness 0.24
3
0.72
2
0.48
1
0.24
Portability
0.19
2
0.38
3
0.57
1
0.19
Accessibility
0.14
2
0.28
3
0.42
1
0.14
Easy to use
0.07
3
0.21
2
0.14
1
0.07
Feasibility
0.063
1
0.063
3
0.189
2
0.126
Sensitivity
0.01
1
0.01
3
0.03
2
0.02
Accuracy
Total
Weight
1.953
2.7
Table 2: scoring Pugh matrix
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1.366
Digital Eye Screening Support System 2019
2.4.
Proposed initial design
In this project we used the system called image acquisition, enhancement and extraction system.
Here, a 3D lens will be attached with a mini camera to acquire high-quality images of the eye. The
acquired images will be enhanced, segmented and extracted to diagonise glaucoma. In addition
sharp IR sensor also be incorporated to acquire intra-ocular presure. Graphical user interface (GUI)
will be develped to acquire image and pressure signal, enhance and exract the image. The system
will be developed using Raspberi pi. Rasperi pi compatible Touch screen LCD display will be used
to display results.the procedure are followed image acquisission, preprocessing, segmentation,
edge detection and cup-to-disc ratio.
Algorithm/ flow chart
Figure 5: algorithm or flow chart
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Digital Eye Screening Support System 2019
2.3.1. Image acquisition
Retinal image of an eye is acquired by USB camera. Cameras typically use 2D (area) Chargecouple device sensors, whereas scanners employ 1D (line) CCDs that move across the image as
each row is scanned. CCDs have become the sensor of choice in many imaging applications
because they do not suffer from geometric distortions and have a linear response to incident light
[18].
Figure 6: image of normal eye and glaucoma eye
2.3.2. Image Enhancement
It includes varying brightness and contrast of image. It also includes filtering and histogram
equalization. It comes under pre-processing step to enhance various features of image. Here we
use gamma correction or power law. The power law transformation function is described by
𝑠 = 𝑐. 𝑟^𝛾
where r is the original pixel value, s is the resulting pixel value, c is a scaling constant, and γ is a
positive value. [18].
2.3.3. Edge detection
Edge detection is a fundamental image processing operation used in many computer vision
solutions. The goal of edge detection algorithms is to find the most relevant edges in an image or
scene. These edges should then be connected into meaning fullness and boundaries, resulting in a
segmented image containing two or more regions [18].
The process of edge detection consists of three main steps [18];
Noise Reduction: Due to the first and second derivative’s great sensitivity to noise, the use of
image smoothing techniques before applying the edge detection operator is strongly
recommended.
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Digital Eye Screening Support System 2019
Detection of Edge Points: Here local operators that respond strongly to edges and weakly
elsewhere are applied to the image, resulting in an output image whose bright pixels are candidates
to become edge points.
Edge Localization: Here the edge detection results are post processed, spurious pixels are
removed, and broken edges are turned into meaningful lines and boundaries, using techniques such
as the Hough transform
There are different types of Edge detection methods
•
First-order derivative edge detection
•
Second-order derivative edge detection
•
The canny edge detector
•
Edge linking and boundary detection
From those we use second-order derivative edge detection, the canny edge detector and edge
linking and boundary detection [18].
2.3.4. Image segmentation
Segmentation is the process of partitioning an image into a set of no overlapping regions whose
union is the entire image. These regions should ideally correspond to objects and their meaningful
parts, and background [18].
Watershed segmentation
Watershed transform decomposes an image completely and thus assigns each pixel either to a
region or a watershed. The watershed transform is used to represent regions in a segmented image
(equivalent to catchment basins) and the boundaries among them (analogous to the ridge lines)
[18].
2.3.5. Cup-to-disc ratio
To calculate the vertical cup to disc ratio, the optic cup and disc first have to be segmented from
the retinal images. The green plane of the registered image is extracted to choose mean value for
background blood vessel, cup and disc are replaced for segmenting the image. Using concatenate
function, the images are mapped in a set of 4 iterations to execute for the above set of mean values.
The identified mean value is replicated with the mean value within each of the array and then the
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Digital Eye Screening Support System 2019
distance matrix is calculated. Small discs have small cups with a median C/D ratio of about 0.35
and Large discs have large cups with a median C/D ratio of about 0.55 [9].
2.3.6. IOP measurement
IOP is determined by the balance between the rate of aqueous secretion and aqueous outflow.
Distribution of IOP is within a range of 11-21mmHg. Normal IOP varies with the time of day,
heartbeat, blood pressure level and respiration. If the value is above this range it viewed with
suspicion [9]. Among glaucoma patients, most of the cases the IOP increase to above 30mmHg,
and some even higher than 100mmHg.we use Arduino and sharp IR sensor to determine the
pressure of the eye by using biomechanics concept which is. 𝜀=𝜎𝑙/Δ𝑙 [19]. 𝜀 is elastic modulus; 𝜎
is stress, and 𝑙is initial length.
Initial length is 10.03cm since the sensor putted at a distance of 10cm and the distance of cornea
to the eyelid is 0.03mm [20]. young’s modulus is 7500.6mmHg [20]. We will get the stress (𝜎)
value from the sensor. Therefore, we can calculate the change in length by using 𝜎=Δ𝑙*𝜀/𝑙 and we
can get new length 𝑙 by adding Δ𝑙 with initial length of 𝑙 = 10.03cm.
2.4. Components lists
2.4.1. Hardware part
•
Lens (Volk 90D)
•
Camera
•
Arduino
•
LCD
•
sharp IR sensor
Lens(volk90D)
The 90D non-contact slit lamp lens is intended for general diagnosis and small pupil examinations,
and features patented double aspheric glass optics that provides enhanced imaging of the retina.
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Digital Eye Screening Support System 2019
Figure 7: volk 90D
Charge coupled device (CCD)
It works by converting light into a pattern of electronic charge in silicon chip. This pattern of
charge is converted into a video wave form, digitalized and stored as an image file on a computer.
Figure 8: CCD
Arduino UNO
Arduino Uno is a microcontroller board based on the ATmega328P (datasheet). It has 14 digital
input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz quartz
crystal, a USB connection, a power jack, an ICSP header and a reset button. It contains everything
needed to support the microcontroller; simply connect it to a computer with a USB cable or power
it with an AC-to-DC adapter or battery to get started [22].
Figure 9: Arduino
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Digital Eye Screening Support System 2019
LCD
LCD is the technology used for displays in note book and other smaller computers. LCD consumes
much less power than LED and gas display because they work on the principle of blocking light
rather than emitting it. In this project 16*2 used for displaying all result, user guide and instruction
for users. We used LCD to display the values of corneal distance and IOP [23].
Figure 10: LCD
Sharp IR sensor
It is a distance measuring sensor unit, it measures from 10cm to 80 cm and composed of an
integrated combination of PSD (positive sensitive detector), IRED (infrared emitting diode) and
signal processing circuit. The variety of the reflectivity of the object, the environmental
temperature and the operating duration are not influenced easily to the distance detection because
of adopting the triangulation method. To convert the distance value to pressure biomechanics
principle is used. Initial length is 10.03cm, young’s modulus is 7500.6mmHg. Distance value can
be acquired from sharp IR sensor and calculate the pressure by using 𝜎=Δ𝑙*𝜀/𝑙 formula. 𝜎 is stress,
Δ𝑙 is change in length, 𝜀 is elastic modulus, and 𝑙 is length. Therefore, the sensor must be very
sensitive for the minimum distance variation.
Figure 11: Sharp IR sensor
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Digital Eye Screening Support System 2019
Potentiometers
It is a three-terminal resistor with a sliding or rotating contact that forms an adjustable voltage
divider If only two terminals are used, one end and the wiper, it acts as a variable resistor. The
measuring instrument called a potentiometer is essentially a voltage divider used for measuring
electric potential (voltage). We used it to adjust brightness of LCD [24].
Figure 12: potentiometer
2.4.2. Software part
MATLAB: (short for MATrix LABoratory) is a special-purpose computer program optimized to
perform engineering and scientific calculations. It started life as a program designed to perform
matrix mathematics, but over the years it has grown into a flexible computing system capable of
solving essentially any technical problem [25].
2.5. Implementation plan
For the implementation of the device, materials are obtained from Biomedical Engineering
laboratories, from Jimma Medical center and also lending some materials. We are supported by
our advisor Dr. Gizeadiss Lamesgin for collecting materials. There were no challenges regard to
materials. After constructing the prototype, we want to test want to test our goals and constraints,
such as accuracy, cost effectiveness, easy to use and sensitivity.
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Digital Eye Screening Support System 2019
CHAPTER THREE
FINAL DESIGN
Our final design was completed as proposed on the first step of designing plan, aimed at obtaining
user friendly, accurate, portable and safe eye screening support system. The design includes
camera and lens holding system, controller system (Arduino and IR sensor) and software. The
wood-made support system is used both as a light guidance system that incorporates slits (small
openings) and also as a support system for the camera and 3D lens. Arduino integrated with the
IR sensor is used to measure intra-ocular pressure. The result of IR sensor is displayed using 16X2
LCD. The software part is designed for ease of use. The Matlab developed GUI includes image
acquisition, Enhancement, Edge detection, Segmentation and analysis part.
3.1. Steps for construction of prototype
Step 1. Material collection that are required for prototype construction from biomedical
department, our advisor and ophthalmology department.
Step 2. Develop flow chart and Algorism for image processing part as well as IOP measurement.
Step 2. Develop MATLAB GUI for image processing.
Step 3. Design the IOP measurement circuit part.
Step 4. Develop Arduino code for IOP measurement.
Step 5. Design lens holder and camera by wood.
Step 6. Capture image and test the system.
Step 7. Connect IOP circuit which are sharp 0A41SKF1Z IR, Arduino and LCD on breadboard.
Step 8. Finally, iteration and test done.
3.2. Iteration and design changes
The device iteration and the modification of equipment listed on the table below.
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Digital Eye Screening Support System 2019
No
Design parts
Proposed
Implemented
1
Arduino Uno
Raspberry pi
Arduino Uno
2
LCD display
Touch screen LCD
16*2
3
Sharp 0A41SKF1Z Sharp 0A41SKF1Z IR Sharp 0A41SKF1Z IR
IR sensor
sensor
sensor
4
Lens
Volk 90D
Volk 90D
5
Camera
Raspberry pi camera
6
Potentiometer
Potentiometer
Remarks
Unavailability
Unavailability
Unavailability
Potentiometer
Table 3: Iteration of the device
Figure 13: prototype construction: A) shows IOP design on Proteus B) Prototype construction of
image C) graphical user interface D) final simulation Part
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Digital Eye Screening Support System 2019
3.3. Final product
Figure 14: final prototype
This system is a dual function system one for image processing part and the other for pressure
measurement. Therefore, two devices function can be substituted by it which are tonometeric
function and slit lamp functions. To achieve this image enhancement, edge detection, segmentation
and cup to disc ratio. Thereby, glaucoma, cataract, ARMD and retinopathy can be diagnosed by
watching the segmented image and cup to disc ratio. The pressure analysis only useful for
glaucoma. On the prototype part simple parts are integrated camera, volk 90D and computer for
processing are integrated. Sharp IR sensor and LCD integrated with Arduino to give IOP for
glaucoma.
3.4. Final components list
1. Arduino Uno
2. LCD 16*2
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Digital Eye Screening Support System 2019
3. Sharp 0A41SKF1Z IR sensor
4. Volk 90D
5. USB cam
3.5. Graphical user interface
The operation window is graphical user interface part. The code is generated using MATLAB it
has eight push button and four axes. Each push buttons are designed for unique function.
Pushbutton 1- 3…. image acquisition
Push button 4…. Image save
Push button 5…. Image Enhancement
Push button 6…. Edge detection
Push button 7 …. Segmentation
Push button 8 …. For analysis of cup to disc ratio and diagnose its normality.
Figure 15: graphical user interface image
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Digital Eye Screening Support System 2019
Image acquisition
Image can be acquired via camera from USB camera or loading image from folder. After Pressing
push button 2 preview will be active and it can load image if we enable the loading portion of code.
It also acquire from camera by enabling capturing code part. Then image can be saved via push
button 4. Figure 16 shows the image acqusition user interface.
Figure 16:image acquisition in GUI
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Digital Eye Screening Support System 2019
Image enhancement
Image enhancement done by using power law, which uses gamma correction factor. It can correct
the camera error or noise by applying correction factor 𝛾 of 1.2. This part enables the physician to
see enhanced image from original. Figure 17 shows the image enhancement user interface.
Figure 17: Image enhancement on GUI
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Digital Eye Screening Support System 2019
Edge detection
Edge detection is found on push button six and it gives out extracted image of the edge. On the
retina edge detection can extract the blood vessels and disc part. This help the physicians to see all
edges clearly. The edge detection processes designed by ‘sobel’ operators, which is one of image
processing technique. Figure 18 shows the edge detection user interface.
Figure 18: edge detection on GUI
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Digital Eye Screening Support System 2019
Image segmentation
It is a major part of image processing part which helps to analyze several kinds of eye diseases by
watching the image. It is found on push button seven and uses watershed algorithm which identify
rough area from smooth one. Therefore, if there is tumor or other odd structure on retinal image,
it shows clearly. On normal condition it segment the disc of retina and display on axes 3. Figure
19 shows the image segmentation user interface.
Figure 19: image segmentation on GUI
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Digital Eye Screening Support System 2019
Analysis
This portion is a final part of image processing which is analysis small diameter circle and large
circle area ratio called cup to disc ratio or CDR. In normal condition cup to disc ratio must be 0.3
and less. If cup increased , CDR also increased which may be an implication of glaucoma. Figure
20 shows the image analysis user interface.
Figure 20: cup to disc ratio on GUI
3.6.
Simulation and results
The IOP sensor and controller are simulated using Proteus software. IR sensor from Proteus is
used to simulate the pressure sensor. The output of the IR sensor is fed to the Arduino. Young’s
modulus of the cornea using biomechanics principle is used to convert the measured distance into
pressure. The output pressure is displayed in LCD. Figure 21 A and B below show the simulation
part.
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Digital Eye Screening Support System 2019
Figure 21: A) Proteus results for normal B) IOP Proteus results for high IOP
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Digital Eye Screening Support System 2019
CHAPTER FOUR
TESTING AND RESULTS
To ensure that a system meets its designed specifications and other requirements, a test plan was
used. Design criteria specified by the developers of the system or project are basement for testing
plan. Specifically, in context of this project definition and contents of design criteria is discussed
below.
4.1. Design criteria
In order to achieve the desired goal of the project, it is mandatory to follow standards that make
listed potential solutions tangible standards and some additional criteria as listed below.
4.1.1. Accuracy
The existing solutions for glaucoma detection is manual, time wasting and physically not safe for
the physician. Due to this the result may face some inaccuracy. Therefore, this project considers
the above features and solves the accuracy problem by capturing image of retina and passes
through several image processing method and give cup to disc ratio in number.
4.1.2. Cost
Engineering designs must be cost effective, Especially, for developing countries as Ethiopia. This
project minimizes the cost of slit lamp and tonometer with simple device. Since its cost effective
it can be used by many health centers in Ethiopia.
4.1.3. Portability
The existing solutions are heavy table top devices this makes them difficult to move. This project
uses light materials and system which is easy to move to other computer or laptop if it has Matlab.
4.1.4. Easy to use
This device is user friendly. It can be used by physicians with simple computer training. For image
acquisition resolution selection can be done by clicking push button. The other push buttons also
have their own functions. Such as, image enhancement, edge detection, segmentation, cup to disc
ratio.
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Digital Eye Screening Support System 2019
4.1.5. Sensitivity
The device must be sensitive for small amount of change corneal distance to calculate the IOP
which is a pre indicator of glaucoma disease. Therefore, sensitivity must fulfill for the device.
4.1.6. Time saving
Time is main thing for health facility since, many patient need to diagnosed and treated as fast as
possible without further process. Although, the existing solutions are manual and time consuming.
But this project gives a digital system which save time and make the diagnosing system easy.
4.2. Testing plan
This section verifies and ensure that a product or system meets its design specifications and other
requirements. A test plan is usually prepared by significant input from group and advisors.
Depending on what is to be tested, different methods will be followed.
4.2.1. Market Testing
Market testing is used in determination of the cost for each component included in the project.
This test is done by summing the price of each component which we use for construction of
prototype and will be comparing with predetermined device cost which was 1710 ETB.
4.2.2. Accuracy test
The main criteria of device to be accepted are accuracy in its measurement. To test the accuracy
of device we use comparison method that is output data from measurement will be compare to the
output of three or four ophthalmologist doctors from slit lamp or existing technology.
4.2.3. Test for user friendly
To test the system can be operated by everyone we invite a guest he/she have no idea about package
or never see before, then asses how much he/she can operate the device without any confusion and
error using graphical user interface.
4.2.3. Sensitivity test
Sensitivity test done by checking the sensor variability for each distance change. To analyze
sensitivity several iterations must be done. After several iterations if it gives precise result it can
be estimated as high sensitive device. If not, it cannot be possible to identify the change in distance
and IOP.
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Digital Eye Screening Support System 2019
4.3. Testing conducted
The following table shows that how criteria are tested and evaluated.
No
Feature to tested
Input
Method
Design
Result
specification
1
Cost
Market
Total
cost < 4181 ETB
1925 ETB
was added
(Which
is
cost
effective)
3
Time
Enumerating
Stop watch
< 1minute
40s
the time
4
User friend
Simple
Train
the Can
procedure
physicians
be Simple
understandable by use
and see how ophthalmologists
they
understand it
Table 4: testing plan and result
37
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Digital Eye Screening Support System 2019
CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
5.1. Conclusion
Human eye is a vital organ of vision which reacts to light and pressure. The eye plays a very
important role not only in life but also in human body, also it gives us the sense of sight, allow us
to see and interpret colors, dimension of object. There are various things that affect structure of
the eye, cause visual impairment and blindness. From them, glaucoma, cataract, age related
macular disease, and retinopathy are the main cause of blindness. Therefore, to diagnose these
diseases early we design an eye screening support system. This system done by image processing
and biomechanics law. Image processing part follows image acquisition, enhancement via power
law, edge detection with Sobel operator, segmentation using watershed algorism and cup to disc
ratio (<=0.3 is normal) and display each result on the computer with graphical user interface. For
each process there is pushbutton which enable it to display the processed image. It has eight push
buttons to enable the image processing axes and image displayed on axes. Biomechanics law used
for glaucoma measurement only since eye pressure is an indicator of glaucoma. This method done
by acquiring corneal distance via sharp IR sensor 0A41SKF1Z and correlate with initial distance,
modules and change in length between original distance and change in length between new
acquired length and the initial length. IOP calculated by the following formula; 𝜎=Δ𝑙*𝜀/𝑙. The
normal range of IOP is from 10-22 mmHg. Segmentation helps to diagnose ARMD and also
retinopathy. Therefore, our device gives a dual purpose and work us a tonometer and slit lamp. To
achieve this material collection from our advisor, biomedical engineering department,
ophthalmology department which is found at Jimma Medical Center and electrical engineering
department. After that we developed MATLAB and Arduino code for graphical user interface of
image processing part and IOP part respectively. Then after, the circuit design on Proteus done
and several iteration process done on the simulation. Finally, connecting components with Arduino
and final prototype done.
After building the prototype, some testing procedures were done. User friendly test, market test,
and user friendly test are among them. The testing result shows that, the device costs 1710 ETB
38
Digital Eye Screening Support System 2019
which is very cost effective, and time saving with in less than 40 seconds and which sensitive as
well as user friendly for the users.
5.2. Recommendations
This project does image processing until segmentation, but it does not classify image as glaucoma,
cataract, ARMD or other for self-diagnosis comparing the processed image with several train
images which saved on that application. Therefore, in the future:
•
Self-diagnosis by using image classification
•
Incorporating the system with mobile application
•
Apply the concept of telemetry and telemedicine.
•
Include temperature difference and other parameters to make IOP measurement more
accurate.
39
Digital Eye Screening Support System 2019
References
[1] A.
A.
Dahl.,
"emedicinehealth,"
19
october
2018.
[Online].
https://www.emedicinehealth.com/anatomy_of_the_eye/article_em.htm/.
Available:
[Accessed
30
march 2019].
[2] C. S. McCaa, "The eye and visual nervous system," Environmental Health Perspectives, vol.
44, pp. 1-8, 1982.
[3] e. Dnyaneshwari D. Patii, "Diagnose glaucoma by proposed image processing method,"
international journal of computer application , Vols. 106-No. 8, p. 14, November, 2014.
[4] J. D. B. et.al, Willy series in pure and applied optics, Newyork, 1975.
[5] Carmen
Willings,
"structure
&
function
of
the
eye,"
[Online].
Available:
http://www.teachingvisuallyimpaired.com/structure-function-of-the-eye.html. [Accessed 28
March 2019].
[6] e. Dr. V. Suresh Babu, "Detection of gaucoma in retinal image using raspberry pi," Asian
journal of applied science and technology (AJAST), vol. 2, no. 2, p. 395, April, 2018.
[7] e.
Coleman
A.,
"Open
opthalmol
J.,"
september
2009.
[Online].
Available:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759104/. [Accessed 1 april 2019].
[8] D. M. Devin, "The demise of direct ophthalmoscopy," april 2015.
[9] A. P. Ken Nischal, Clinical Opthalmology, UK: Elsevier, 2011.
[10] G. Bruno, "Eye disorders," huntington college of health sciences , 2009.
[11] "ophalmology procedures manual," national health and nutrition examination survey, 2005.
[12] d. o. opthalmology, "ophtalmology and eye disease," 2016-2017.
[13] G. Nicole, "what is slit lamp," april 2018.
40
Digital Eye Screening Support System 2019
[14] W. L. M. A. MD, "Principles of Gonioscopy," Color Atlas of Gonioscopy, 2017.
[15] R. Kara, "ophthalmoscope," Encylopaedia britannica, 05 November 2014. [Online].
Available:
https://www.britannica.com/technology/ophthalmoscope#accordion-article-
history. [Accessed 02 April 2019].
[16] A. C. Imitiaz, "measurement of central corneal thickness in health and disease," pp. 179-180,
23 october 2009.
[17] S. B. a. A. Agarwal, "opthalmic instrument and diagnostic tes," color atlas of
ophthalmoscopy , pp. 488-506.
[18] O. Marques, practical image and video processing using matlab, florida: JOhn Wiley and
Sons, Inc, 2011.
[19] L. Waite, Applied biofluid Mechanics, USA, 2007.
[20] W. A. v. d. Bosch, "topographic anattomy of the eye lids, and the effects of sex and age," Br
ophthalmol, vol. 83, pp. 347-352, 1999.
[21] a. alsheikh, "determination of modulus of elastisity if the human cornea," journal of
refractory surgery, 2007.
[22] "Arduino UNO Rev3," [Online]. Available: https://store.arduino.cc/arduino-uno-rev3.
[Accessed 24 May 2019].
[23] 2. c. L. d. w. y. b. light. [Online]. Available: https://www.hwkitchen.com/products/lcddisplay-2*16-characters/. [Accessed 24 may 2019].
[24] J. Yong, testine electronic components.
[25] S. J.chapman, matlab programing with applicaions for engineers, USA: global engineering,
2013.
[26] N. Christian, "What is glaucoma," 15 December 2017.
41
Digital Eye Screening Support System 2019
[27] M. A. e. Abiye, "epidimology of glaucoma in central Ethiopia," Ethiopia , 2009.
[28] B. e. Frank, "imaging technologies for the diagnosis of Glaucoma," p. 16, 21 December 2009.
42
Digital Eye Screening Support System 2019
Appendices
Appendix A
Pair wise comparison chart
no Feature
Sensitivity Easy Accuracy Accessibility Feasibility portability Cost
to
Total
effective
use
1
Sensitivity
X
0
0
0
0.17
0
0
0.17
2
Easy to use
1
X
0
0
0.5
0
0
1.5
3
Accuracy
1
1
X
1
1
1
1
6
4
Accessibility 1
1
0
X
1
0
0
3
5
Feasibility
0.83
0.5
0
0
X
0
0
1.33
6
Portability
1
1
0
1
1
X
0
4
7
Cost
1
1
0
1
1
1
X
5
effective
Table 5: pair wise comparison chart
Total =21
➢ Weight = feature/total
43
Digital Eye Screening Support System 2019
Appendix B
Sharp IR sensor datasheet
Figure 22: Sharp IR sensor Datasheet A
Figure 23:Sharp IR sensor Datasheet B
44
Digital Eye Screening Support System 2019
Appendix C
Camera specification
Figure 24: camera specification
45
Digital Eye Screening Support System 2019
Appendix D
Arduino code
int IR=A0;
float distancevalue;
float length_change;
float IOpressure;
float initial_length=10.03;
float Y=7500.6;
LiquidCrystal lcd(12,11,5,4,3,2);
void setup() {
lcd.begin(16,2);
}
void loop() {
distancevalue=analogRead(IR)*0.0048828125;
length_change=distancevalue-initial_length;
IOpressure=(length_change*Y)/initial_length;
lcd.setCursor(0,0);
lcd.print("corneadis=");
lcd.print(distancevalue);
lcd.setCursor(12,0);
lcd.print("cm");
lcd.setCursor(0,1);
lcd.print("IOP=");
46
Digital Eye Screening Support System 2019
lcd.print(IOpressure);
lcd.setCursor(10,1);
lcd.print("mmHg");
if(IOpressure<10)
{
lcd.setCursor(12,1);
lcd.print("LowP");
delay(50);
}
if(IOpressure>=10 && IOpressure<=22)
{
lcd.setCursor(12,1);
lcd.print("well");
delay(50);
}
if(IOpressure>22)
{
lcd.setCursor(12,1);
lcd.print("HighP");
delay(50);
}
}
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Digital Eye Screening Support System 2019
Appendix E
MATLAB code
function varargout = Glacouma(varargin)
% GLACOUMA MATLAB code for Glacouma.fig
%
GLACOUMA, by itself, creates a new GLACOUMA or raises the existing
%
singleton*.
%
H = GLACOUMA returns the handle to a new GLACOUMA or the handle to
%
the existing singleton*.
%
GLACOUMA('CALLBACK',hObject,eventData,handles,...) calls the local
%
function named CALLBACK in GLACOUMA.M with the given input arguments.
%
GLACOUMA('Property','Value',...) creates a new GLACOUMA or raises the
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help Glaucoma
% Last Modified by GUIDE v2.5 08-Jun-2019 15:00:29
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',
mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @Glacouma_OpeningFcn, ...
gui_State.gui_Callback = str2func(varargin{1});
end
gui_mainfcn(gui_State, varargin{:});
end
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Digital Eye Screening Support System 2019
% End initialization code - DO NOT EDIT
% --- Executes just before Glacouma is made visible.
function Glacouma_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject
handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
% varargin command line arguments to Glacouma (see VARARGIN)
%set(handles.axes1,'Visible','off')
%set(handles.axes2,'Visible','off')
% Choose default command line output for Glacouma
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes Glacouma wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = Glacouma_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject
handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
49
Digital Eye Screening Support System 2019
% handles
structure with handles and user data (see GUIDATA)
D=menu('Select Video Input Device:',IA);
if isempty(IA)||D==0
msgbox
IA=IA(D,:);
IA(IA==' ')=[];
x=imaqhwinfo(IA);
try
DeviceID=menu('Select Device ID',x.DeviceIDs);
F=x.DeviceInfo(DeviceID).SupportedFormats;
nF=menu('Select FORMAT',F);
warndlg({'Try Another Device or ID ';...
'You Donot Have Installed This Device(VideoInputDevice)'})
return
end
% --- Executes on button press in axes1button.
function pushbutton2_Callback(hObject, eventdata, handles)
global IA DeviceID Format s
try
VidObj= videoinput(IA, DeviceID, Format);
nBands = get(handles.VidObj, 'NumberOfBands');
set(handles.axes1,'Visible','off')
axes(handles.axes1)
50
Digital Eye Screening Support System 2019
preview(handles.VidObj, hImage)
catch E
msgbox({'Configure The s Correctly!',' ',E.message},'s INFO')
end
guidata(hObject, handles);
% --- Executes on button press in pushbutton6.
function pushbutton9_Callback(hObject, eventdata, handles)
% hObject
handle to pushbutton9 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
s=getimage(handles.axes1);
s=rgb2gray(s);
%s=im2double(s);
%gradient
gmag = imgradient(s);
%watershade
L = watershed(s);
Lrgb = label2rgb(L);
%opening - closing reconstruction
Iobrd = imdilate(Iobr,se);
%Regional Maxima of Opening-Closing by Reconstruction
fgm = imregionalmax(Iobrcbr);
%Regional Maxima Superimposed on Original Image
51
Digital Eye Screening Support System 2019
%I2 = labeloverlay(Io,fgm);
se2 = strel(ones(5,5));
fgm2 = imclose(fgm,se2);
fgm3 = imerode(fgm2,se2);
%Modified Regional Maxima Superimposed on Original Imag
fgm4 = bwareaopen(fgm3,20);
%I3 = labeloverlay(s,fgm4);
%Thresholded Opening-Closing by Reconstruction
%bw = imbinarize(Iobrcbr);
threshold = graythresh(Iobrcbr);
bw=im2bw(Iobrcbr,threshold);
%Watershed Ridge Lines
D = bwdist(bw);
DL = watershed(D);
bgm = DL == 0;
% Compute the Watershed Transform of the Segmentation Function.
gmag2 = imimposemin(gmag, bgm | fgm4);
L = watershed(gmag2);
%Markers and Object Boundaries Superimposed on Original Imag
labels = imdilate(L==0,ones(3,3)) + 2*bgm + 3*fgm4;
%I4 = labeloverlay(s,labels);
%Colored Labels Superimposed Transparently on Original Image
52
Digital Eye Screening Support System 2019
axes(handles.axes3)
%hold on
%himage = imshow(Lrgb);
%himage.AlphaData = 0.3;
% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
% hObject
handle to pushbutton5 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
s=getimage(handles.axes1);
F= s;%imresize(F,0.5);
axes(handles.axes2)
imshow(I,'parent',handles.axes2)
% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
% hObject
handle to pushbutton6 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
%%%%%%%%%% DialateBloodVessel( RGB )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
s=getimage(handles.axes2);
%s=rgb2gray(s);
axes(handles.axes2)
53
Digital Eye Screening Support System 2019
imshow(BW1,'parent',handles.axes2);
% --- Executes on button press in pushbutton5.
function pushbutton8_Callback(hObject, eventdata, handles)
% hObject
handle to pushbutton8 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
s=getimage(handles.axes2);
%s=rgb2gray(s);
imshow(F,'parent',handles.axes6)
hold on
viscircles(centers, radii,'EdgeColor','b');
%As an alternative, you can use the builtin imfindcircles functions as follows
% calculate the centroid and radius of all the regions
%stats = regionprops('table',grad >= 20,'Centroid', 'MajorAxisLength','MinorAxisLength');
%centers = stats.Centroid;
%diameters = mean([stats.MajorAxisLength stats.MinorAxisLength],2);
%radii = diameters/2;
%[maxRadii, iMax] = max(radii); % select the largest circle
%subplot(2, 2, 1);
%viscircles(centers(iMax, :),maxRadii); % visualise the selected circle
function edit1_Callback(hObject, eventdata, handles)
% hObject
handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
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Digital Eye Screening Support System 2019
% handles
structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit1 as text
%
str2double(get(hObject,'String')) returns contents of edit1 as a double
% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
% hObject
handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
%
See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on mouse press over figure background.
function figure1_ButtonDownFcn(hObject, eventdata, handles)
% hObject
handle to figure1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
function edit4_Callback(hObject, eventdata, handles)
% hObject
handle to edit4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit4 as text
55
Digital Eye Screening Support System 2019
%
str2double(get(hObject,'String')) returns contents of edit4 as a double
% --- Executes during object creation, after setting all properties.
function edit4_CreateFcn(hObject, eventdata, handles)
% hObject
handle to edit4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
%
See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on selection change in listbox4.
function listbox4_Callback(hObject, eventdata, handles)
% hObject
handle to listbox4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns listbox4 contents as cell array
%contents{get(hObject,'Value')} returns selected item from listbox4
% --- Executes during object creation, after setting all properties.
function listbox4_CreateFcn(hObject, eventdata, handles)
% hObject
handle to listbox4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
empty - handles not created until after all CreateFcns called
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Digital Eye Screening Support System 2019
% Hint: listbox controls usually have a white background on Windows.
%
See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton9.
function pushbutton10_Callback(hObject, eventdata, handles)
% hObject
handle to pushbutton10 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles
structure with handles and user data (see GUIDATA)
57
Digital Eye Screening Support System 2019
Appendix F
Statement of approval from ophthalmology department
Figure 25: statement of approval from JUSH ophthalmology ward
58
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