Vision machine learning for detection of ocular pathologies from iris images. (2nd January 2020)

• Record Type: Journal Article
• Authors: Sujatha, K.; Thivya, K. S.; Anand, M.; Jayachitra, N.; Durgadevi, G.; Bhavani, N. P. G.; Srividhya, V.
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 23:Number 1(2020)
• Page Start: 145
• Page End: 155
• Publication Date: 2020-01-02
• Subjects: 68T10; Eye disease -- Blind de-convolution -- Wavelet transform -- Principal Component analysis and fuzzy C-means clustering algorithm; Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2020.1721878
• Languages: English
• ISSNs: 0972-0529

Abstract

Abstract: Detection of eye pathologies from the database of iris images is taken into deliberation. The images of disease affected and normal eyes are collected from High-Resolution Fundus (HRF) Image Database is analyzed and the influence of ocular diseases on iris using a reliable Fuzzy recognition scheme is proposed here. These eye images are then subjected to various image processing techniques like pre-processing for de-noising using Blind de-convolution, wavelet based feature extraction, Principal Component Analysis (PCA) for dimension reductionality, followed by fuzzy C-means clustering inference scheme to categorize the normal and diseased eyes. It is inferred that the proposed method takes only 2 minutes with an accuracy, specificity and sensitivity varying in the range of 94% to 98% respectively.