please send me the matlab code for cataract detection
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Diagnostic images are often evaluated for clinical outcomes using subjective methods, which are limited by the reviewer's ability. Computer assisted diagnosis (CAO) algorithms that help reviewers in their outcome decisions have been developed to increase sensitivity and specificity in the clinical setting. However, these systems have not been well used in research scenarios to improve the measurement of clinical end points. Reductions in bias through its use could have important implications for etiological research.
The eye is a very important organ of the human body, which has many complex sensory elements such as lens, retina etc. Eye disorder is a prominent issue in the health care sector. Cataract is an eye disorder, which occurs due to opacity of the lens. Over a period of time, the cataract will lead to reduced vision. If the cataract is not treated at the appropriate time, then it will lead to blindness. This is common in the elderly. In this work, image processing techniques are used to detect the characteristics in the three kinds of optical eye images such as normal, cataract and post-cataract images. The characteristics of the optical eye image are extracted, such as large ring area (BRA), small ring area (SRA), edge pixel count (EPC) and object perimeter. The characteristics are analyzed statistically and are significant for automatic classification. The same features are then used in the automatic sorter such as Vector Support Machines (SVM) for automatic sorting. The results were found to be clinically significant with a sensitivity of 94% and a specificity of 93.75%.