MRI images segmentation using improved spatial FCM clustering and pillar algorithms

Abstract

The segmentation of brain tissue from MRI images is a vast subject of study, a critical task and a very important issue for different medical applications; however, its numerous problems remain relatively open. In this paper, the main purpose of the project is to carry out a new segmentation technique based on a combined method between pillar algorithm and spatial fuzzy c-means clustering. The proposed approach applies FCM clustering to image segmentation after optimised by pillar algorithm in terms of initial centres precision and computational time. The features of the segmented brain image are extracted in different classes [white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF)] using the integrating elements interpreted to get partially or fully automated tools allowing a correct extraction of the cerebral tissue. The developed algorithm has been implemented and the program is run through a Simulink in MATLAB. All experimental results are very satisfactory which allows us to clarify that using a combined method of several segmentation algorithms allow to get better results and improve the classification.