Influence of Segmentation over Magnetic Resonance Image Classification

Abstract

Magnetic resonance imaging is an image based diagnostic technique which is widely used in medical environment. Thus, the efficient automated analysis of this kind of images is of great importance for both, scientific and clinical environment. In this paper, analysis of evaluation results of the classification of magnetic resonance images with different classifiers is conducted. This analysis is provided in both cases, with and without application of graph-based segmentation technique. The aim of the paper is to investigate whether or not this kind of segmentation technique induces improvements in the classification of MRIs. Seven descriptors are used for feature extraction in our paper, and the classification is analyzed in all seven cases. The ultimate goal of the paper is to signify in which combination of classification technique and feature extraction algorithm, the examined segmentation technique is the most appropriate for magnetic resonance images. For the overall investigation in this paper, a specific hierarchical organized dataset of magnetic resonance images is used.