Optimized clustering segmentation using heuristic algorithm (HA) with fcm for brain image analysis

The objective of this proposed work is to progress brain image segmentation methods for medical
imaging applications, using Fuzzy based clustering segmentation approaches. The main aim is to
propose a brain epileptic segmentation system suited for MRI processing using temporal filter. The
resolution is to simply segment epilepsy in MRI with reproducible outcomes. Cluster analysis recognizes
collections of comparable objects and therefore helps in learning circulation of outlines in big data sets.
Clustering is most widely used for real world applications. However, accuracy of these algorithms for
abnormal brains with edema, tumor, epilepsy, etc., is not efficient because of limitation in initialization
of this algorithm. In this research work, FCM based techniques have been proposed to improve the
efficiency of clustering approaches. The main focus of the work, based on human MRI brain image, is to
optimize the segmentation process with higher accuracy rate, for finding the epileptic tissues of the
brain, by using computational intelligence and image processing techniques.