Abstract- In recent years an image segmentation describe the process ofsplit an image into equally restricted regions. It can be measured as thelargest part of vital and key process for facilitating the demarcation,characterization, and hallucination of regions in many medical images. Thereare lots of techniques on hand for image segmentation but still it desires toexpand well-organized and speedy techniques for medical image segmentation. Aresourceful image segmentation technique introduced using K-means clusteringtechnique integrated with Fuzzy C-means algorithm has been proposed here. Tooffer precise brain tumour discovery system a thresholding and level set segmentationstages has been introduced. By using this K-means clustering the computationtime can be minimised. Subsequently a fuzzy -C means has been added to get theaccurate result. The system performance has been evaluated through thecomparative analysis of the proposed image segmentation approach with respectaccuracy and processing time.
Keywords— k-means,FCM-Fuzzy _C means, KIFCM-K integrated fuzzy –C means, Morphological operation.