Principal Component Analysis

Abstract

In recent years segmentation approaches based on sequential Monte Carlo Methods delivered promising results for the localization and delineation of anatomical structures in medical images. Also known as Shape Particle Filters, they were used for the segmentation of human vertebrae, lungs and hearts, being especially well suited to cope with the high levels of noise encountered in MR data and difficult overlaps in radiographs. This report surveys the robustness of these methods on different medical example images. A Differential Evolution approach on Shape Particle Filtering is applied for image segmentation. The goal of this work is to analyze the behavior, e.g. the robustness of the implemented Shape Particle Filter. Results on different data (synthetic rectangles, MRI slices and radiographs) are reported.