Contour-Based Image Segmentation Using Selective Visual Attention
Engin Mendi, Mariofanna Milanova
DOI: 10.4236/jsea.2010.38092   PDF    HTML     4,806 Downloads   10,046 Views   Citations


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.

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Mendi, E. and Milanova, M. (2010) Contour-Based Image Segmentation Using Selective Visual Attention. Journal of Software Engineering and Applications, 3, 796-802. doi: 10.4236/jsea.2010.38092.

Conflicts of Interest

The authors declare no conflicts of interest.


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