Hi..I need cataract detection matlab code. Can you help please..

Abstract

Ocular Cataract are the leading cause of blindness. The diagnosis of cataracts is costly for poor people. The authors used a novel digital image processing technique in order to diagnose cataracts. For their study, the authors used digital images of 6 eyes (6 patients) with varying degrees of nuclear cataract, 4 eyes (4 patients) with varying cortical cataracts, and 3 normal eyes (3 healthy people). The pictures were converted to greyscale and then to binary formats for finding the relative variations in pixel intensities between healthy and cataractous eyes. The three normal eyes served as reference. Hence, the software was able to detect the presence of cataracts. Digital ‘masks’ were used for edge detection (boundary between healthy and cataractous portions of eyes) and subsequently classify the cataract into nuclear or cortical. Then the severity of each cataract was determined and its contour plotted. The technique was verified using prior physicians’ evaluations. Two test cases were used to perform clinical evaluations of the technique. The technique has 94.96% accuracy and 95.14% reliability. This paper deals with automatic grading of nuclear cataract (NC) from slit-lamp images in order to reduce the efforts in traditional manual grading. Existing works on this topic have mostly used brightness and color of the eye lens for the task but not the visibility of lens parts. The main contribution of this paper is in utilizing the visibility cue by proposing gray level image gradient-based features for automatic grading of NC. Gradients are important for the task because in a healthy eye, clear visibility of lens parts leads to distinct edges in the lens region, but these edges fade as severity of cataract increases. Experiments performed on a large dataset of over 5000 slit-lamp images reveal that the proposed features perform better than the state-of-the-art features in terms of both speed and accuracy. Moreover, fusion of the proposed features with the prior ones gives results better than any of the two used alone.