Mathematical Techniques for Biomedical Image Segmentation

Abstract

This article presents some mathematical methods for biomedical image segmentation that these authors have used, adapted, and developed. In particular, some segmentation strategies for blood vessel, atherosclerosis, and intracerebral hemorrhage images constitute one of the principal causes of death in all those countries where the classical epidemics do not have an important weight. In the field of biomedical, images have developed sophisticated algorithms for image segmentation, which go from the deformable models, bioinspired algorithms, and neural networks, among others. Many of these strategies for arriving at satisfactory results need a lot of computational time. For this reason, the proposal of simple, fast, and reliable algorithms for biomedical image segmentation will be always welcome. The remainder of the article is presented in section “Materials and Methods.” Section “Mathematical Techniques Theory” outlines some mathematical techniques and theoretical aspects. In section “Algorithms,” we describe some of our algorithms. Section “Segmentation of Blood Vessel Images” demonstrates the experimental results, comparisons, and discussion. Finally, in section “Conclusions,” the most important conclusions are given.