MRI Image Segmentation Using Unsupervised Clustering Techniques
D. Selvathi, A. Arulmurgan, S. Thamarai Selvi, S. Alagappan · 2006
In medical image visualization and analysis, segmentation is an indispensable step in the processing of images. MR has become a particularly useful medical diagnostic tool for cases involving soft tissues, such as in brain imaging. The aim of our research is to develop an effective algorithm for the segmentation of the MRI images. This paper discusses the use and implementation of fuzzy C means clustering and genetic algorithm (GA) for an automatic segmentation of white matter (WM), gray matter (GM), cerebro spinal fluid (CSF), the extra cranial regions and the presence of tumor regions. The results were analyzed and compared with the reference "gold standard" obtained from radiologists.