Mammographic image segmentation with modified FCM based clustering algorithm

As medical image segmentation is the present day challenge and a demand for researchers, several attempts have been made in this field. Fuzzy c-means clustering (FCM) is an algorithm that is affected by noise, thus objective-function is generally initiated with local-spatial information to enhance the robustness. But it’s implementation often results in rise in time complexity, caused by a repetitive computation of the distance separating pixels of local-spatial neighbours and clustering-centres. In order to solve this problem, a Modified FCM (MFCM) method is proposed in this p aper. I nitially, Image e nhancement is performed using Contrast Limited Adaptive Histogram Equalization (CLAHE) then, the local-spatial data of images is subsumed into the MFCM algorithm with introduction of morphological-reconstruction operation (morphological opening operation) to assure detail preservation and noise immunity of images. Thirdly, alteration of membership partition build on the distance between pixels enclosing local-spatial neighbours and centres of clusters is exchanged with local membership filtering. Algorithm is quite efficient as median-membership filtering is capable of improvising partition matrix s ystematically. Hence, we propose that the explained algorithm achieves better results in less time.