MRI Brain image segmentation with supervised SOM and probability-based clustering method

Authors:
Andres Ortiz;Juan M. Gorriz;Javier Ramirez;Diego Salas-Gonzalez
Affiliations:
Communications Engineering Department, University of Malaga, Malaga, Spain;Department of Signal Theory, Communications and Networking, University of Granada, Granada, Spain;Department of Signal Theory, Communications and Networking, University of Granada, Granada, Spain;Department of Signal Theory, Communications and Networking, University of Granada, Granada, Spain
Venue:
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Year:
2011

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Abstract

Nowadays, the improvements in Magnetic Resonance Imaging systems (MRI) provide new and aditional ways to diagnose some brain disorders such as schizophrenia or the Alzheimer's disease. One way to figure out these disorders from a MRI is through image segmentation. Image segmentation consist in partitioning an image into different regions. These regions determine diferent tissues present on the image. This results in a very interesting tool for neuroanatomical analyses. Thus, the diagnosis of some brain disorders can be figured out by analyzing the segmented image. In this paper we present a segmentation method based on a supervised version of the Self-Organizing Maps (SOM). Moreover, a probability-based clustering method is presented in order to improve the resolution of the segmented image. On the other hand, the comparisons with other methods carried out using the IBSR database, show that our method ourperforms other algorithms.