Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features

Abstract

Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational decision support systems for the early detection of glaucoma can help prevent this complication. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry, and Heidelberg retina tomography scanning methods. In this paper, we present a novel method for glaucoma detection using a combination of texture and higher order spectra (HOS) features from digital fundus images. Support vector machine, sequential minimal optimization, naive Bayesian, and random-forest classifiers are used to perform supervised classification. Our results demonstrate that the texture and HOS features after z-score normalization and feature selection, and when combined with a random-forest classifier, performs better than the other classifiers and correctly identifies the glaucoma images with an accuracy of more than 91%. The impact of feature ranking and normalization is also studied to improve results. Our proposed novel features are clinically significant and can be used to detect glaucoma accurately.

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

“…Two parameters energy and contrast were considered to be the most efficient for discriminating different textural patterns. A novel glaucoma detection system using a combination of texture and higher order spectra features proposed by Rajendra et al (2011) provided an accuracy of more than 91%. Yogesh and Sasikala (2012) described texture analysis of retinal layers in spectral domain OCT in which diagnosis of age related macular degeneration, diabetic macular edema was tested.…”

Glaucoma Detection Using Dwt Based Energy Features and Ann Classifier

“…1. Fundus images (a) normal (b) glaucoam.In glaucoma,the pressuers within the eye's vitreous chamber increases and compromises the blood vessels of the optic nerve head,leading to permanent loss of axonsof the vital ganglion cells they are stored in lossless JPEG format [10].In this dtaset it contain 60 fundus images: 30 normal and 30 open angle glaucomatous images from 20 to 70 years old.For diagnosing this disease,a fundus camer,microscope and a light source used. Fig.…”

“…Finally, we comparing this results with those obtained using HOS and texture features [10]. The training samples and the test sample identifiers are mapped with those used in and it will enable a direct comparision of accuracy.…”

“…In this paper we use image processing for classifying normal and glaucomatous images accurately for that we use the wavelet energy of each wavelet subband by implementing wavelet packet based texture classification [9] for differentiating the potential of the texture features obtained from the image [10]. It is based on the distribution of average texture features obtained from three prominent wavelet families.…”

Empirical Wavelet Transform and GLCM Features Based Glaucoma Classification From Fundus Image

“…Detection of glaucoma using Higher Order Spectra features (HOS) and Texture features is discussed in [7]. The method for glaucoma detection using a combination of texture and HOS features from digital fundus images.…”