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AUTOMATIC RECOGNITION OF EXUDATIVE MACULOPATHY USING FUZZY C- MEANS CLUSTERING AND NEURAL NETWORKS

Platform : DSP

AIM: To analyze and understand color normalization and contrast enhancement of the objects. ABSTRACT Abstract. Retinal exudates are typically manifested as spatially random yellow/white patches of varying sizes and shapes. They are a characteristic feature of retinal diseases such as diabetic maculopathy. An automatic method for the detection of exudate regions is introduced comprising image colour normalisation, enhancing the contrast between the objects and background, segmenting the colour retinal image into homogenous regions using Fuzzy C-Means clustering, and classifying the regions into exudates and non exudates patches using a neural network. Experimental results indicate that we are able to achieve 92 % sensitivity and 82 % specificity. LEARNING OBJECTIVE: Verification of Fuzzy C-Means clustering, and classifying the regions in the image using neural network. Input: Any raw image Output : MACULOPATHY version of the image Applications: Image processing, Video processing and transmission Software tool used: MATLAB 2007A

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