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

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. 1. Introduction 1.1. Problem Statement The main objective of this work is the description of our methodology for automatic classification of brain tissues. In concrete terms, a use of Vector Quantization Networks (VQNs) in the automatic nonsupervised characterization of tissue in the human head is proposed. It is expected, by means of nonsupervised classification, that brain regions presenting similar features are properly grouped. In such sense, the idea to be developed here considers the application of a similarity metric which is sustained in the use of the Discrete Compactness (DC). The DC is a factor that shares information about the shape of an object. It was proposed originally by Bribiesca [1] and it is inspired by the familiar Shape Compactness of an object. However, it has a greater robustness because it has a low sensitivity to variations, in the shape of an object, produced due to noise or capture defects. The original specification for Kohonen’s training algorithm considered the use of the Euclidean Distance as similarity metric in order to identify the so-called Winner Neuron. Several works also mention that the use of another metrics in order to achieve this task is possible: the Canberra Distance [2], the Sup Distance (a special case of the Minkowski Distance also known as Chebyshev Distance) [3], the Manhattan Distance [4], and the Dot Product [2]. In this regard, we have done some experiments of this kind; see [5]. The metric to be used depends on the specific characteristics of the classification task to be performed by a VQN. In our case we aim to characterize cerebral tissue by