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Retinal Exudate Detection using Novel Fuzzy Clustering Methods

Ravindraiah R and Chandra Mohan Reddy M

At present there is an increased interest in Therapeutic image processing in most fields of engineering. Imaging modality provides detailed information about anatomy. It is also helpful in the finding of the disease and its progressive treatment. The Prime signs of Diabetic Retinopathy (DR) are Exudates which leads to severe vision loss in chronic condition. Exudates are the remnant of exuded blood and protein based particles from the damaged blood vessels of retina. Laser healing requires accurate location of exudates for faithful removal through Laser burns. Segmentation of fundoscope image will help the ophthalmologist in diagnosis, classification and to determine the severity. Multiple methods are designed and developed for Medical Image segmentation based on thresholding, Region Growing, Markov Random Model, Clustering, Deformable Model, Classifier, Neural Networks, Expectation Maximization and Support vector machines etc. Out of these the fuzzy clustering methods are less complex and are robust in operation. This paper aims in performance evaluation of Fuzzy C means clustering (FCM) algorithm, Kernel induced FCM (KFCM) and Spatial FCM (SFCM) algorithms are done.