Abstract
This paper proposes and evaluates an algorithm to automatically detect the cataracts from color images in adult human subjects. Currently, methods available for cataract detection are based on the use of either fundus camera or Digital Single-Lens Reflex (DSLR) camera; both are very expensive. The main motive behind this work is to develop an inexpensive, robust and convenient algorithm which in conjugation with suitable devices will be able to diagnose the presence of cataract from the true color images of an eye. An algorithm is proposed for cataract screening based on texture features: uniformity, intensity and standard deviation. These features are first computed and mapped with diagnostic opinion by the eye expert to define the basic threshold of screening system and later tested on real subjects in an eye clinic. Finally, a tele-ophthamology model using our proposed system has been suggested, which confirms the telemedicine application of the proposed system.
Highlights
Eye related disorders and subsequent vision loss affects individuals severely through on-going medical expenses during treatment of disorders and through the high economic and mental trauma of degrading vision [1]
Depending upon cataract occurrence in the eye we have developed a classification of cataracts which classifies the images into: Nuclear, Cortical and Post Subcapsular Cataract (PSC)
This paper presented a texture information based automated algorithm for detection of cataracts from a digital eye image of adult human subjects
Summary
Eye related disorders and subsequent vision loss affects individuals severely through on-going medical expenses during treatment of disorders and through the high economic and mental trauma of degrading vision [1]. All the discussed works related to computer aided cataract detection use retinal, ultrasound or slit lamp images. These systems have increased complexities, and the cost of acquisition module is very high. The main limitation of these discussed systems lies in determining the accurate thresholds value of multiple texture parameters such as uniformity, standard deviation and mean intensity of pupil area in digital eye image. This requires exhaustive training and validation of the developed detection algorithms using diagnostic opinion from an ophthalmologist.
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