A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Perceptual image representation
Journal on Image and Video Processing
IEEE Transactions on Image Processing
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Telemedicine is growing and there is an increased demand for faster image processing and transmitting diagnostic medical images. Identifying and extracting the region of interest (ROI) accurately is an important step before coding and compressing the image data for efficient transmission or storage. The usual approach to extract ROI is to apply contour segmentation method. Chan-Vese active contour model [1] is a well-known image segmentation technique based on Mumford-Shah level set methods. Selective visual attention is a fundamental component of perceptual representation in a visual system. It influences the identification of a stimulus from those that operate after perception is complete. The Saliency Toolbox [2] is a collection of Matlab functions and scripts for computing the saliency map for an image, for determining the extent of a proto-object, and for serially scanning the image with the focus of visual attention. The implementation of the toolbox is extension of the saliency map-based model of bottom-up attention [3], 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. 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. Finally, we successfully segmented two attended locations of a medical image.