A Hierarchical Topological Knowledge Based Image Segmentation Approach Optimizing the use of Contextual Regions of Interest : Illustration for Medical Image Analysis

This paper concerns image segmentation and presents a method to automically determine optimal regions of interest (ROI) according to topological information. The use of ROI avoids the processing of irrelevant image points, therefore improving and accelerating segmentations. ROI determination is based on the optimal use of both the a priori knowledge about topological structure of an image and the contextual information. Contextual information concerns the nature of already segmented regions in the case of the hierarchical segmentation approach we consider. We describe this general purpose method and propose a formulation for the optimal determination of ROIs according to both informations. Then, we illustrate the use and the implementation of such a method in the particular case of medical image segmentation.

1. Introduction

Most works related to image segmentation aim at finding a method allowing to simultaneously split an image into its constitutive regions. In such a case, the difficulty is to design an algorithm that can simultaneously discriminate the different groups of image points, thanks to appropriate features (e.g. textural or photometric ones). Such an approach fails when regions share too similar properties.