Abstract
Extraction of ROI (Region-Of-Interest) in dermatosis images can be used in content-based image retrieval (CBIR). Image segmentation takes an important part in it. And the performance of the segmentation algorithm directly influences the efficiency of the ROI extraction results. In this paper, a method of Chan-Vese segmentation based on saliency detection to extract the ROI of the dermatosis images is proposed. Firstly the spectral residual approach (SR) [11] is used to get the saliency map of the dermatosis images. Secondly threshold segmentation is used to get the initial ROI images. Finally the Chan-Vese model is used to segment the initial ROI images to get the final ROI images, which can ensure the active contours evolve close to the object and remove the redundant information from the complex background. The experiment results show that the proposed method has the better performance than only using Chan-Vese method.
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Wang, Z., Zhu, L., Qi, J. (2014). ROI Extraction in Dermatosis Images Using a Method of Chan-Vese Segmentation Based on Saliency Detection. In: Park, J., Adeli, H., Park, N., Woungang, I. (eds) Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40675-1_31
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DOI: https://doi.org/10.1007/978-3-642-40675-1_31
Publisher Name: Springer, Berlin, Heidelberg
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