Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 638563, 12 pages
Research Article

Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors

1Communications Engineering Department, University of Malaga, 29004 Malaga, Spain
2Department of Signal Theory, Communications and Networking, University of Granada, 18060 Granada, Spain

Received 12 February 2013; Accepted 15 April 2013

Academic Editor: Anke Meyer-Baese

Copyright © 2013 Andrés Ortiz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE) and has been assessed using real brain images from the Internet Brain Image Repository (IBSR).