Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted from Fundus Images

ABSTRACT:

Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve subsequently causes loss of vision. The available scanning methods are Heidelberg Retinal Tomography (HRT), Scanning Laser Polarimetry (SLP) and Optical Coherence Tomography (OCT). These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an 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 LSSVM is employed for classification with Radial Basis Function (RBF), Morlet wavelet and Mexican-hat wavelet kernels. The classification accuracy of proposed method is 98.33% and 96.67% using three-fold and ten-fold cross validation respectively.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• The paper discussed the system for the automated identification of normal and glaucoma classes using Higher Order Spectra (HOS) and Discrete Wavelet Transform (DWT) features.

• The extracted features are fed to the Support Vector Machine (SVM) classifier with linear, polynomial order 1, 2, 3 and Radial Basis Function (RBF) to select the best kernel function for automated decision making.

• In this work, SVM classifier with kernel function of polynomial order 2 was able to identify the glaucoma and normal images automatically. 

DISADVANTAGES OF EXISTING SYSTEM:

• This segmentation has shortcomings like localization, thresholding or demarcation which may lead to unacceptable results and unavoidable errors in glaucoma diagnosis.

PROPOSED SYSTEM:

• In this work, we are proposing a novel method for the classification of glaucoma images based on EWT and correntropy features.

• EWT decomposes the image into various frequency bands. Correntropy is extracted from the decomposed EWT components. The features are normalized and ranked on the basis of significant criteria.

• Least Squares Support Vector Machine (LS-SVM) classifier with various kernels such as Radial Basis Function (RBF) and wavelet kernels such as Morlet and Mexican-hat are used for the classification.

• Three-fold and ten-fold cross validation strategies are used to develop the automated glaucoma diagnosis system.

ADVANTAGES OF PROPOSED SYSTEM:

The advantages of the proposed method are as follows:

(i) Obtained a sensitivity of 100% indicating that, there is no false negative. This will reduce the workload of clinicians by more than 50%. Now they need to focus their attention only on normal class.

(ii) Reported high performance using three-fold and ten-fold cross validation. Hence, the proposed method is repeatable.

(iii) Used less number of features to obtain highest classification accuracy. Hence, the developed system is simple and fast.

(iv) Validated our proposed system using both private and public databases.

(v) Performance of the system does not depend on the resolution of the image.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System          : Pentium Dual Core.

• Hard Disk      : 120 GB.

• Monitor          : 15’’ LED

• Input Devices : Keyboard, Mouse

• Ram                : 1GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : MATLAB

• Tool                      : MATLAB R2013A

REFERENCE:

Shishir Maheshwari, Ram Bilas Pachori, and U. Rajendra Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted from Fundus Images”, IEEE Journal of Biomedical and Health Informatics, 2017.