Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features

Authors:
Muthu Rama Krishnan Mookiah;U. Rajendra Acharya;Choo Min Lim;Andrea Petznick;Jasjit S. Suri
Affiliations:
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore;Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore;Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore;Ocular Surface Research Group, Singapore Eye Research Institute, Singapore;Fellow AIMBE, CTO, Global Biomedical Technologies Inc., Roseville, CA, USA and Biomedical Engineering Department, Idaho State University (Aff.), ID, USA
Venue:
Knowledge-Based Systems
Year:
2012

Citing 21
Cited 3

Digital Image Processing

Digital Image Processing
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms

Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Automatic Detection of Microaneurysms in Color Fundus Images of the Human Retina by Means of the Bounding Box Closing

ISMDA '02 Proceedings of the Third International Symposium on Medical Data Analysis
Prediction model building and feature selection with support vector machines in breast cancer diagnosis

Expert Systems with Applications: An International Journal
Identification of different stages of diabetic retinopathy using retinal optical images

Information Sciences: an International Journal
Clinical practice guidelines: A case study of combining OWL-S, OWL, and SWRL

Knowledge-Based Systems
Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images

Journal of Medical Systems
Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages

Journal of Medical Systems
Statistical analysis of mammographic features and its classification using support vector machine

Expert Systems with Applications: An International Journal
Automated Diagnosis of Glaucoma Using Digital Fundus Images

Journal of Medical Systems
A framework for detecting glaucomatous progression in the optic nerve head of an eye using proper orthogonal decomposition

IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Classifying glaucoma with image-based features from fundus photographs

Proceedings of the 29th DAGM conference on Pattern recognition
A support vector machine-based model for detecting top management fraud

Knowledge-Based Systems
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging

Knowledge-Based Systems
Palmprint verification based on 2D - Gabor wavelet and pulse-coupled neural network

Knowledge-Based Systems
Automatic microcalcification and cluster detection for digital and digitised mammograms

Knowledge-Based Systems
Data mining techniques for the screening of age-related macular degeneration

Knowledge-Based Systems
Wavelets and filter banks: theory and design

IEEE Transactions on Signal Processing
Pattern recognition using invariants defined from higher order spectra: 2-D image inputs

IEEE Transactions on Image Processing
Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features

IEEE Transactions on Information Technology in Biomedicine
Wavelet-Based Energy Features for Glaucomatous Image Classification

IEEE Transactions on Information Technology in Biomedicine

Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach

Knowledge-Based Systems
Computer-aided diagnosis of diabetic retinopathy: A review

Computers in Biology and Medicine
Review: Knowledge discovery in medicine: Current issue and future trend

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Eye images provide an insight into important parts of the visual system, and also indicate the health of the entire human body. Glaucoma is one of the most common causes of blindness. It is a disease in which fluid pressure in the eye increases gradually, damaging the optic nerve and causing vision loss. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods. These methods are expensive and hence a novel low cost automated glaucoma diagnosis system using digital fundus images is proposed. The paper discusses 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 with an accuracy of 95%, sensitivity and specificity of 93.33% and 96.67% respectively. Finally, we have proposed a novel integrated index called Glaucoma Risk Index (GRI) which is made up of HOS and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images.