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12 March 2010 Cervigram image segmentation based on reconstructive sparse representations
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Abstract
We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable. By leveraging sparse representations the data can be transformed to higher dimensions with sparse constraints and become more separated. K-SVD algorithm is employed to find sparse representations and corresponding dictionaries. The data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we applied our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of 60,000 images of the uterine cervix. Compared with other general methods, our approach showed lower space and time complexity and higher sensitivity.
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Shaoting Zhang, Junzhou Huang, Wei Wang, Xiaolei Huang, and Dimitris Metaxas "Cervigram image segmentation based on reconstructive sparse representations", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762313 (12 March 2010); https://doi.org/10.1117/12.845461
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