vii
TABLE OF CONTENTS
CHAPTER
TITLE
DECLARATION
ii
DEDICATION
iii
ACKNOWLENGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xi
LIST OF FIGURES
xiii
LIST OF ABBREVIATIONS
xviii
LIST OF SYMBOLS
LIST OF APPENDICES
1
2
PAGE
xx
xxiii
INTRODUCTION
1
1.1
Research Background
1
1.2
Problem Statement
4
1.3
Objectives
4
1.4
Scope of Research
5
1.5
Thesis Organization
6
1.6
Contribution of Research
6
LITERATURE REVIEW
8
2.1
Anatomy of Normal Kidney
8
2.2
Review of Kidney Test and Diagnosis Techniques
9
2.2.1
10
Blood Test
viii
2.3
2.2.2
Urine Test
12
2.2.3
Kidney Biopsy
13
2.2.4
Imaging Tests
14
Ultrasound Imaging for Kidney Diagnosis
16
2.3.1
Review of Ultrasound
17
2.3.2
Kidney Risks, Abnormalities and Diseases
20
2.3.3
Ultrasound Image Features
27
2.3.4
Kidney Ultrasound Diagnosis: Variation
and Misdiagnosis
2.4
Kidney Ultrasound Image Processing and
Analysis
2.4.1
3
29
32
Image Enhancement and Speckle
Reduction
33
2.4.2
Image Segmentation
34
2.4.3
Vector Graphic Image
37
2.4.4
Automatic Kidney Region of Interest
Generation
41
2.4.5
Feature Extraction of Kidney US Images
42
2.4.6
Computer Aided Diagnosis (CAD)
43
2.5
Artificial Neural Network (ANN)
46
2.6
Summary
48
EXPERIMENTAL DESIGN AND
IMPLEMENTATION
50
3.1
Introduction
50
3.2
Data Acquisition
52
3.3
Vector Graphic Image Formation
54
3.4
Automatic Region of Interest Generation of
3.5
Kidney Ultrasound Images
63
3.4.1
Seed Region Selection
65
3.4.2
Active Contour Rough Segmentation
67
Automatic Detection and Segmentation of Kidney
Cysts in Ultrasound Images
68
ix
3.6
4
Feature Extraction, Selection and Kidney
Ultrasound Image Classification
72
3.6.1
Image Pre-Processing
74
3.6.2
Feature Extraction
77
3.6.3
Feature Selection
81
3.6.4
Image Classification
81
RESULT AND DISCUSSION
86
4.1
Introduction
86
4.2
Vector Graphic Image Formation
86
4.2.1
89
4.3
Parameter Optimization
Experiment Result of Automatic Kidney ROI
Generation
4.4
4.5
4.6
5
92
Experiment Result of Automatic Detection and
Segmentation of Kidney Cysts
95
4.4.1
Evaluation Metrics
98
4.4.2
Comparison with Other Segmentation
Methods
100
4.4.3
Multiple Cysts Detection
104
4.4.4
Statistical Tests
106
4.4.5
Sensitivity Analysis
111
4.4.6
Limitations
113
Experiment Result of Feature Extraction,
Selection and ANN-based Classification
114
4.5.1
Result of Feature Extraction
115
4.5.2
Feature Selection
118
4.5.3
ANN-based Classification
119
Summary
124
4.6.1
126
Usefulness Index
CONCLUSION AND RECOMMENDATION
128
5.1
Conclusion
128
5.2
Recommendation
130
x
REFERENCES
Appendices A – E
133
148-176
xi
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
Classification of CKD
11
2.2
Kidney diseases affecting kidney size
21
2.3
Comparison of detection of renal stones using
ultrasound and CT
30
The interrater reliability for antenatal hydronephrosis
diagnosis of 50 anteroposterior renal pelvis diameter
measurements [174]
31
Summary of segmentation approach for kidney
ultrasound images
36
2.6
Comparison of available vector graphic software
39
2.7
Performance levels of CAD schemes for differential
diagnosis
44
2.8
Tests for Kidney Diagnosis
48
3.1
MSE and PSNR value of three different filters applied
to kidney ultrasound images
59
3.2
ANN data sampling
83
4.1
Vector graphic image of different values of Ncolor and
respective execution time
91
4.2
Validation of automatic ROI generation
94
4.3
Comparison of error metrics of the active contour
method by Chan and Vese [168], level set method by
Li et al. [170], and the proposed method
103
2.4
2.5
4.4
Comparison of time complexity of the Active Contour
method [168], Level-Set method [170], and the
xii
proposed method
103
Determination of TP, FP, FN and TN during cysts
detection
106
Performance evaluation of developed algorithm for
single cyst detection
108
4.7
Algorithm testing to 25 multicysts kidney images
110
4.8
Summary findings of Ncolor and R value for detection
of cysts in different organs
113
Manual assessment of three image classes (NR, BI and
CD) by experts
115
Mean and standard deviation value of all features for
three classes of kidney ultrasound images
116
4.11
Student t-test results for NR, BI and CD
118
4.12
Classification result of the neural network using
significant features
124
Classification result of the neural network using vector
graphic features
124
4.5
4.6
4.9
4.10
4.13
xiii
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
2.1
Anatomy structure of a normal kidney
9
2.2
Healthy and damaged kidney during albumin
filteration
12
2.3
Real time kidney biopsy using ultrasound
13
2.4
Kidney images of (a) IVP, (b) CT, (c) MRI, and (d)
Ultrasound
15
Measurement of kidney length (A), width (B) as
well as volume (C) using Toshiba Aplio MX
ultrasound machine
16
2.6
Doppler ultrasound showing normal resistive index
17
2.7
Frequency of sound waves for ultrasound system
(adapted from [49])
18
2.8
Nomenclature related to ultrasound resolution
19
2.9
An example of ectopic kidney (arrow); smaller in
size and abnormally rotated [68]
22
Example of ADPKD [18]; (a) Illustration of
polycystic kidney, (b) White line shows US calipers
used for measuring purpose (cyst and size) while
black line shows some of the cysts detected
23
An example of mutiple cysts image, with clear
visualization of cysts, renal pyramids as well as
calyces
25
2.5
2.10
2.11
2.12
Example of acute pyelonephritis kidney image [73].
Left image is 2-dimensional image while right
image is Doppler ultrasound image. Yellow arrows
in both left and right images show the infected area.
xiv
For Doppler US image, red and blue colors indicate
the blood flow of the area where blue is when the
blood flow away from the transducer while red is
when the blood flow towards the transducer.
26
Ultrasound image of kidney with stones. White
arrow shows the kidney stone
27
2.14
A normal kidney ultrasound images
28
2.15
Example of vector graphic transformation using
market software (a), (c), (e) and (g) Original bitmap
image (b) Vectorization using Adobe Illustrator
[154], (d) Vectorization using CorelDRAW [155]
(f) Vectorization using Vector Magic [113], and (h)
Vectorization using AutoTrace [140]
40
An overall system for breast cancer screening using
ultrasound images, consists of an ultrasound
imaging device, a whole-breast scanning device and
CAD system [162]
45
Basic diagram of an artificial neural network with
one input layer, 2 hidden layers and one output
layer. The hidden layers employ a sigmoid and
linear transfer function to adjust weight (W) and
biases (b) [132]
47
3.1
Workflow design for the study
51
3.2
Sample US images of (a) Normal kidney, (b)
Abnormal kidney with infection, and (c) Abnormal
kidney with cysts
53
3.3
Flowchart of vector graphic image formation
54
3.4
Formation of vector graphic image with different
value of Ncolor
55
3.5
RGB Color Cube for uint8 Images [63]
56
3.6
Minimum variance quantization on a slice of the
RGB color cube [63]
57
Kidney ultrasound, (a) Original image, (b) Wavelet
filtering output, (c) Median filtering output, and (d)
Wiener filtering output
59
3 layers of black and white image for Ncolor =3, (a)
Layer 1, (b) Layer 2, (c) Layer 3
60
2.13
2.16
2.17
3.7
3.8
xv
3.9
3.10
3.11
3.12
Shapes plotting of P = [0; 0; 1; 1], T = [0; 1; 1; 0],
and C = 1
62
Vector graphic image of kidney ultrasound using
Ncolor = 3
62
Flowchart for
generation
63
automatic
region
of
interest
Ultrasound Image of (a) normal kidney. Normal
kidney always have clear and separate regions of
sinus and cortex (b) cystic kidney. Appearance of
cyst in kidney leads to unclear separate regions of
sinus and cortex
64
(a) Black and white image after thresholding, (b)
output of filtered image that intersect with center
window
65
(a) Image with two remaining regions (region 1 and
region 2), (b) region 1 was selected as winning
region, (c) Image with three remaining regions
(region 1, region 2, and region 3), (b) region 1 was
selected as winning region
67
Kidney ultrasound image with, (a) single cyst, and
(b) multiple cysts
69
Steps for kidney cysts automatic detection and
segmentation
70
Roundness test for both kidney ultrasound image of
single and multiple cysts
72
3.18
Steps for kidney ultrasound image classification
73
3.19
Edge detection of kidney in ultrasound image (black
contour)
74
Rotation to zero degree (a) Orientation before
rotation, (b) Position of Ymin, Ymax, Xmin and Xmax, (c)
Orientation after rotation
75
3.21
Image rotation to zero degree
75
3.22
Output image after cropping
76
3.23
An image with removed background
76
3.24
Development of GLCM from input, I image, with
3.13
3.14
3.15
3.16
3.17
3.20
xvi
different orientation (0, 45, 90, and 135) ;
adapted from [63]
78
Kidney vector graphic images for different class
(NR, BI and CD) for different value of Ncolor
(1,2,3,4,5)
80
3.26
Overall ANN development process
82
4.1
(a) Input image of real kidney, (b) Input image of
kidney ultrasound, (c) Output image of (a) after
vectorization using Vector Magic, (d) Output image
of (b) after vectorization using Vector Magic, (e)
Output image of (a) after vectorization using
proposed algorithm, and (f) Output image of (b)
after vectorization using proposed algorithm
88
Vector graphic image of different value of Ncolor
(Ncolor = 1 to Ncolor = 12)
90
(a) Input and (b) Vector graphic image with Ncolor =
3, (c) Winning seed region, (d) Active contour
rough segmentation result, and (e) Output image of
automatic region of interest generation
93
4.4
Example of false positive ROI generation
94
4.5
Kidney images with manually detected cysts
boundary by group of experts for (a) single cyst,
and (b) multiple cysts
96
(a) Input image of kidney with single cyst, (b)
vector graphic image, (c) Image after binarization,
(d) Image after filtering, (e) Cyst detection and
segmentation
97
Areas of TP, FP and FN in image with manual and
automatic contours
99
Segmentation result of (a) manual contour by
sonographer, (b) proposed method, (c) active
contour method by Chan and Vese [168], (d) levelset method by Li et al. [170]
102
(a) Input image of kidney with multiple cysts, (b)
vector graphic image, (c) Image after binarization,
(d) Image after filtering, and (e) Cyst detection and
segmentation
105
3.25
4.2
4.3
4.6
4.7
4.8
4.9
4.10
Example of mutiple cysts kidney images; (a) Cyst
xvii
4.11
4.12
4.13
4.14
4.15
4.16
4.17
4.18
4.19
detection by experts (white borders), (b) Cyst
detection by developed algorithm (red borders),
with true positive cyst regions (red), false negative
cyst region (yellow) and false positive cyst region
(blue) (TP = 8, FP = 1, and FN = 1)
109
(a) Input, and (b) output image of segmentation of
liver cyst (R = 0.55)
112
(a) Input and (b) output images of segmentation of
thyroid cyst (R = 0.61)
112
(a) Input and (b) output images of segmentation of
multiple cysts in ovary (0.55 ≤ R ≤ 1)
112
Output image of multile cysts kidney with
unsuccessful (yellow circle) and wrong (blue circle)
detection of cysts.
114
Mean value for three classes of kidney ultrasound
images for (a) Intensity histogram features, (b)
GLCM features, and (c) Vector graphic features
117
MSE and testing performance for different neuron
number in the hidden layer
120
MSE and testing performance for different value of
learning rate
121
MSE and testing performance for different value of
iteration rate
122
MSE and testing performance for different value of
momentum constant
123
xviii
LIST OF ABBREVIATIONS
3D
-
Three dimensional
AC
-
Active contour
ADPKD
-
Autosomal dominant polycystic kidney disease
AI
-
Adobe Illustrator
ANN
-
Artificial neural network
ARPKD
-
Autosomal recessive polycystic kidney disease
BI
-
Bacterial Infection
BUN
-
Blood urea nitrogen
CAD
-
Computer aided diagnosis
CBIR
-
Content-based image retrieval
CC
-
Cortical cyst
CD
-
Cystic disease
CKD
-
Chronic kidney disease
CT
-
Computed tomography
DICOM
-
Digital imaging and communication in medicine
ESRD
-
End stage renal disease
FBME
-
Faculty of Biosciences and Medical Engineering
FN
-
False negative
FP
-
False Positive
GFR
-
Glomerular filtration rate
GLCM
-
Gray level co-occurrence matrix
GUI
-
Graphical user interface
HD
-
Hausdorff distance
IVP
-
Intravenous pyelogram
MDRD
-
Modification of diet in renal disease
MLP
-
Multilayer perception
xix
MRD
-
Medical renal disease
MRI
-
Magnetic resonance imaging
MRF
-
Markov random field
MSE
-
Mean squared error
NR
-
Normal
PCA
-
Principal component analysis
PDF
-
Portable document format
PSNR
-
Peak signal to noise ratio
RGB
-
RGB image
RI
-
Resistive index
ROC
-
Receiver operating characteristic
ROI
-
Region of interest
RRT
-
Renal replacement therapy
SI
-
Similarity index
SPL
-
Spatial pulse length
SVG
-
Support vector graphics
SVM
-
Support vector machine
TP
-
True positive
US
-
Ultrasound
VBA
-
Visual basic for application
VUR
-
Vesicoulateral reflux
WV
-
Weight vector
xx
LIST OF SYMBOLS
A
-
Area
Am
-
Pixel set of manual outline
Aa
-
Pixel set of automatic outline
Az
-
Average ROC curve area
b
-
Biases
BW
-
Body weight
cm
-
Centimeter
Cn
-
Contrast
Cr
-
Correlation
dB
-
Decibel
dl
-
Deciliter
d(pj, Q)
-
Shortest distance of pj to contour Q
E
-
Energy
EQ
-
Equality test
F
-
Number of cycle
GLCM(i,j)
-
GLCM image
gm
-
Gram
H
-
Homogeneity
IIN
-
Input image
ILAYERED(i)
-
Layered image
IORI
-
Original image
IVG
-
Vector graphic image
IWF(i,j)
-
Wiener filtered image
kHz
-
Kilohertz
K(m,n)
-
Kurtosis
m
-
Meter
xxi
MAP
-
Array
mg
-
Milligram
MHz
-
Megahertz
min
-
Minute
ml
-
Milliliter
n
-
Number of bits
MN
-
Size of image
Ncolor
-
Number of colors
Npixel
-
Number of pixel
NR
-
Number of pixel in region
Nshape
-
Number of shape
p
-
Perimeter
R
-
Roundness test
Rrank
-
Region rank
$
-
US Dollar
Scr
-
Serum creatinine
S(m,n)
-
Skewness
TC
-
Threshold
TT
-
Threshold
u(i,j)
-
Discrete image
v
-
Speed
v2
-
Noise variance
VG2R
-
Vector graphic ratio in 2nd layer
VG3R
-
Vector graphic ratio in 3rd layer
VG4R
-
Vector graphic ratio in 4th layer
w
-
Weight
x(i,j)
-
Original image
Xmax
-
Maximum value of X
Xmin
-
Minimum value of X
y(i,j)
-
Output Image
Ymax
-
Maximum value of Y
Ymin
-
Minimum value of Y
z
-
Axial resolution
xxii
-
Local mean
-
Mean
σ2
-
Local variance
σ2(m,n)
-
Variance
θ0
-
Angle
-
Wavelength
(m,n)
xxiii
LIST OF APPENDICES
APPENDIX
TITLE
PAGE
A
Data, Machines and Operators
148
B
Ultrasound Scanning Procedure
151
C
Source Code for Image Segmentation
169
D
Gantt Chart
174
E
List of Publications
175