Title :
MRI image segmentation using unsupervised clustering techniques
Author :
Selvathi, D. ; Arulmurgan, A. ; Thamarai Seivi, S. ; Alagappan, S.
Author_Institution :
Dept. of Electron. & Commun. Eng., MEPCO Schlenk Eng. Coll., Tamilnadu, India
Abstract :
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.
Keywords :
biomedical MRI; brain; genetic algorithms; image segmentation; medical image processing; pattern clustering; tumours; unsupervised learning; MRI image segmentation; brain imaging; cerebro spinal fluid; fuzzy C means clustering; genetic algorithm; gray matter; medical diagnostic tool; medical image visualization; unsupervised clustering technique; white matter; Biological tissues; Biomedical imaging; Brain; Clustering algorithms; Image analysis; Image segmentation; Magnetic resonance imaging; Medical diagnosis; Medical diagnostic imaging; Visualization; Fuzzy C Means; Genetic Algorithm; Homomorphic Filtering; MR Imaging; Segmentation;
Conference_Titel :
Computational Intelligence and Multimedia Applications, 2005. Sixth International Conference on
Print_ISBN :
0-7695-2358-7
DOI :
10.1109/ICCIMA.2005.40
