A Scalable Framework For Segmenting Magnetic Resonance Images
Journal of Signal Processing Systems
Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means
Fundamenta Informaticae
Robust Motion Detection via the Fuzzy Fusion of 6D Feature Space Decompositions
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A novel cell segmentation method and cell phase identification using Markov model
IEEE Transactions on Information Technology in Biomedicine
Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means
Fundamenta Informaticae
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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.