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

Abstract

Glaucoma is a multifactorial optic neuropathy disease characterized by elevated Intra Ocular Pressure (IOP). As the visual loss caused by the disease is irreversible, early detection is essential. Fundus images are used as input and it is preprocessed using histogram equalization. First order features from histogram and second order features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the preprocessed image as textural features reflects physiological changes in the fundus images. Second order textural features are extracted for different quantization levels namely 8, 16, 32, 64, 128 and 256 in four orientations viz 0, 45, 90 and 135° for various distances. Extracted features are selected using Sequential Forward Floating Selection (SFFS) technique.The selected features are fed to Back Propagation Network (BPN) for classification as normal and abnormal images. The proposed computer aided diagnostic system achieved 96% sensitivity, 94% specificity, 95% accuracy and can be used for screening purposes. In this study, the analysis of gray levels have shown their significance in the classification of glaucoma.

THE RELATIONSHIP BETWEEN HETEROSIS AND GENETIC DISTANCES BASED ON SSR MARKERS IN <i>HELIANTHUS ANNUUS</i>

“…Highly productive heterotic hybrids can be obtained only from parents with genetically predetermined combining ability. Introduction of DNA-Science Publications AJABS marker analyses into biological studies opened new opportunities for investigation of heterosis role in selective process; particularly for development of parental inbred lines and analysis of their combining ability (Lariepe et al, 2012;Daisy and Selvi, 2014;Karthikeyan and Rengarajan, 2014).…”

Medical Image Segmentation for Anatomical Knowledge Extraction

“…This deletion process reduces the ROI size significantly and thereby reduces the computation complexity and increases the performance of the segmentation algorithm. Median filtering is an edge enhancement technique (Karthikeyan and Rengarajan, 2014). All these three sequential steps are performed for all the given images.…”

“…This module includes Image resizing, histogram equalization, ROI selection (Image cropping) and median filtering. In our method, a global Histogram equalization (Yousuf and Rakib, 2011;Karthikeyan and Rengarajan, 2014) is used which is a perfect technique for contrast and texture enhancement of medical images. Based on the anatomical knowledge, as much as unnecessary information should be removed from the images in the preprocessing stage.…”

An Innovative Approach for Person Identification by Detection and Extraction of Optic Disc from Retina and Concha from Ear

“…Abaza et al (2013) presented a systematic discussion which includes available databases, detection and feature extraction techniques, as well as a survey of some unsolved ear recognition problems. An American police officer (Iannarelli, 1989), proposed a first nonautomated ear recognition system based on a set of 12 measurements. Prior works on this include those by (Victor et al, 2002) on Principal Component Analysis of ear images and a further improvement of the same by (Chang et al, 2003).…”

“…Karthikeyan and Rengarajan (2014) developed a glaucoma diagnosis system which classifies the retinal images based on the behavior of texture features as a function of gray level quantization. Kavitha and Duraiswamy (2011) proposed an automated system to extract blood vessels and detect exudates to screen diabetic retinopathy.…”