2-D Compact Variational Mode Decomposition Based Automatic
Classification of Glaucoma Stages From Fundus Images
Suku Krishna K V
M.TECH S3 DOI
Guide-Mr. Kiran Babu(Assistant professor)
Reg.no-47321007
Index
 Abstract
 Introduction
 Literature review
 Existing Method
 Drawbacks
 Proposed method
 Advantages
 Applications
 Hardware and Software Requirement
 Conclusion
 References
Abstract:
 Glaucoma is one of the leading causes of vision loss worldwide.
 This problem can be reduced by the early and reliable diagnosis of glaucoma.
 Here a method to classify the glaucoma stages (healthy, early-stage, and advanced stage) using a 2-D compact
variational mode decomposition (2-D-C-VMD) algorithm is done.
 The preprocessed input images are first decomposed into several variational modes (VMs) employing 2-D-CVMD.
 Finally, a trained multiclass least-squares-support vector machine (MC-LS-SVM) classifier has been utilized
for classification purpose.
Introduction:
 Glaucoma is a group of eye diseases, which results in damage to the optic nerve. The main
risk factor is increased intraocular pressure (IOP) in the eye.
 The disorders can be divided into two major groups, namely, primary open-angle glaucoma
(POAG) and primary angle closer glaucoma (PACG).
 Glaucoma can permanently damage the vision of the affected eye because the effect of
glaucoma is gradually increased, which is difficult to identify earlier until the circumstance
is at a critical stage.
 It is necessary to have regular eye examinations so that diagnosis can be made in its early
stages and treated appropriately.
Literature Review:
S. No
Journal Type with year
Authors
Title
Outcomes
Studied about disc-
IEEE Trans. Med. Imag., vol.
1
Disc-aware ensemble
aware ensemble
37, no. 11, pp. 2493–2501, Nov.
H. Fu et al.
network for glaucoma
network for
2018.
screening from fundus image
glaucoma screening.
in Proc. 1st IEEE Int. Conf.
Meas., Instrum., Control
2
Automated classification of
Studied about
glaucoma using retinal
classification of
fundus images
glaucoma
D. Parashar and D.
Autom. (ICMICA),
Agrawal
Kurukshetra, India, Jun. 2020,
pp. 1–6.
Studied about
IEEE Trans. Inf. Technol.
3
S. Dua, U. R. Acharya, P.
Wavelet-based energy
glaucomatous image
Biomed., vol. 16, no. 1, pp. 80–
Chowriappa, and S. V. Sree
features for glaucomatous
classification using
87, Jan. 2012
image classification
wavelet
Literature Review:
S.
No
Journal Type with year
Authors
Title
Outcomes
Detecting glaucoma progression using
A. T. Nguyen, D. S.
Ophthalmology
4
guided progression analysis with OCT
Greenfield, A. S.
Glaucoma, vol. 2, no. 1,
and visual field assessment in eyes
Bhakta, J. Lee, and
pp. 36–46, Jan. 2019.
classified by international classification
Studied about
glaucoma stages
progression
W. J. Feuer
of disease severity codes
Med. Eng. Phys., vol. 34,
5
T.-C. Lim, S.
A survey and comparative study on the
no. 2, pp. 129–139, Mar.
Chattopadhyay, and
instruments for glaucoma detection
2012.
U. R. Acharya
Automated diagnosis of glaucoma using
IEEE J. Biomed. Health
6
S. Maheshwari, R. B.
empirical wavelet transform and
Informat., vol. 21, no. 3,
Pachori, and U. R.
correntropy features extracted from
pp. 803–813, May 2017.
Acharya
fundus images
It’s a comparative
study glaucoma
detection
Studied about
empirical wavelet
transform and other
feature extracting
methods for
diagnosing glaucoma.
Existing Method:
 Automated diagnosis of glaucoma using digital fundus images based on empirical wavelet transform
(EWT).
 The EWT is used to decompose the image, and correntropy features are obtained from decomposed EWT
components.
 These extracted features are ranked based on t value feature selection algorithm.
 Then, these features are used for the classification of normal and glaucoma images using least-squares
support vector machine (LS-SVM) classifier.
 The LS-SVM is employed for classification with radial basis function, Morlet wavelet, and Mexican-hat
wavelet kernels.
Disadvantages in Existing Method:
EMD approach having drawbacks such as
•
boundary distortion
•
noise sensitivity (Sn)
•
mode mixing
•
the lack of mathematical proof.
Proposed Method:
Block Diagram of Proposed Method
Proposed Method:
• Support Vector Machine(SVM) is a supervised machine learning algorithm used for both classification
and regression.
• The goal of the SVM algorithm is to create the best line or decision boundary that can segregate ndimensional space into classes so that it will easily put the new data point in the correct category in the
future. This best decision boundary is called a hyperplane.
• Least-squares support-vector machines (LS-SVM) are least-squares versions of support-vector
machines (SVM).
• Here one finds the solution by solving a set of linear equations instead of a convex quadratic
programming (QP) problem for classical SVMs.
Proposed Method:
• Least-squares SVM classifiers were proposed by Johan Suykens and Joos Vandewalle. LS-SVMs are a
class of kernel-based learning methods.
• Green channel images have been extracted from the RBG image because it contains finer details for
down-streaming analysis.
• Further, Using contrast-limited histogram equalizations (CLAHE) to improve contrast and pixel
intensity. Then, 2-D-C-VMD has been used for ID.
• Then, various features are computed from the first variational mode (VM).
• Then, linear discriminant analysis (LDA) has been applied for the reduction of dimensionality.
• Afore, a trained multiclass least squares-support vector machine (MC-LS-SVM) classifier has been
utilized for the classification task.
Advantages of Proposed Method:
SVM Classifier
• SVM works relatively well when there is a clear margin of separation between classes.
• SVM is more effective in high dimensional spaces.
• SVM is effective in cases where the number of dimensions is greater than the number of samples.
• SVM is relatively memory efficient
• A prevalent and effective decomposition method is the variational mode decomposition (VMD).
• Compared to EMD decomposition, VMD has excellent noise resistance, better decomposing performance,
and stability and can be also utilized for feature extraction and fault diagnose
Applications of Proposed Method:
• In medical fields
• In early stage of glaucoma
Hardware & Software Requirements:
Software: Matlab R2020a or above
Hardware:
Disk:
Operating Systems:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8
• Windows 10
GB for a typical installation
• Windows 7 Service Pack 1
Recommended: An SSD is recommended A full installation
• Windows Server 2019
of all MathWorks products may take up to 29 GB of disk
• Windows Server 2016
space
Processors:
RAM:
Minimum: Any Intel or AMD x86-64 processor
Minimum: 4 GB
Recommended: Any Intel or AMD x86-64 processor with
four logical cores and AVX2 instruction set support
Recommended: 8 GB
Conclusion
 A newly introduced 2-D-C-VMD-based algorithm has been used for ID
 It has various advantageous properties such as sharp boundaries, fully
adaptive, and non-recursive multiresolution technique.
 VMD has been employed to decompose preprocessed fundus images into
different VMs.
 It will have less computation complexity with better Ac and speed.
 Effective for early and more accurate detection of glaucoma.
References:
[1] H. Fu et al., “Disc-aware ensemble network for glaucoma screening from fundus image,” IEEE Trans. Med.
Imag., vol. 37, no. 11, pp. 2493–2501, Nov. 2018.
[2] D. Parashar and D. Agrawal, “Automated classification of glaucoma using retinal fundus images,” in Proc.
1st IEEE Int. Conf. Meas., Instrum., Control Autom. (ICMICA), Kurukshetra, India, Jun. 2020, pp. 1–6.
[3] S. Dua, U. R. Acharya, P. Chowriappa, and S. V. Sree, “Wavelet-based energy features for glaucomatous
image classification,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 1, pp. 80–87, Jan. 2012.
References:
[4] A. T. Nguyen, D. S. Greenfield, A. S. Bhakta, J. Lee, and W. J. Feuer, “Detecting glaucoma progression
using guided progression analysis with OCT and visual field assessment in eyes classified by international
classification of disease severity codes,” Ophthalmology Glaucoma, vol. 2, no. 1, pp. 36–46, Jan. 2019.
[5] T.-C. Lim, S. Chattopadhyay, and U. R. Acharya, “A survey and comparative study on the instruments for
glaucoma detection,” Med. Eng. Phys., vol. 34, no. 2, pp. 129–139, Mar. 2012.
[6] S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated diagnosis of glaucoma using empirical
wavelet transform and correntropy features extracted from fundus images,” IEEE J. Biomed. Health Informat.,
vol. 21, no. 3, pp. 803–813, May 2017.