Table of Contents
Advances in Artificial Neural Systems
Volume 2013 (2013), Article ID 278241, 18 pages
Research Article

Novel Discrete Compactness-Based Training for Vector Quantization Networks: Enhancing Automatic Brain Tissue Classification

Computer Engineering Institute, The Technological University of the Mixteca (UTM), Carretera Huajuapan-Acatlima Km 2.5, 69004 Huajuapan de León, OAX, Mexico

Received 27 June 2013; Revised 19 September 2013; Accepted 18 November 2013

Academic Editor: Juan Ignacio Arribas

Copyright © 2013 Ricardo Pérez-Aguila. 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.


An approach for nonsupervised segmentation of Computed Tomography (CT) brain slices which is based on the use of Vector Quantization Networks (VQNs) is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC) which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric. Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.