Contour-Based Image Segmentation Using Selective Visual Attention

Abstract

In many medical image segmentation applications identifying and extracting the region of interest (ROI) accurately is an important step. The usual approach to extract ROI is to apply image segmentation methods. In this paper, we focus on extracting ROI by segmentation based on visual attended locations. Chan-Vese active contour model is used for image segmentation and attended locations are determined by SaliencyToolbox. The implementation of the toolbox is extension of the saliency map-based model of bottom-up attention, by a process of inferring the extent of a proto-object at the attended location from the maps that are used to compute the saliency map. When the set of regions of interest is selected, these regions need to be represented with the highest quality while the remaining parts of the processed image could be represented with a lower quality. The method has been successfully tested on medical images and ROIs are extracted.