27 October 2013 PLSA-based pathological image retrieval for breast cancer with color deconvolution
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Proceedings Volume 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing; 89200L (2013) https://doi.org/10.1117/12.2032054
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
Digital pathological image retrieval plays an important role in computer-aided diagnosis for breast cancer. The retrieval results of an unknown pathological image, which are generally previous cases with diagnostic information, can provide doctors with assistance and reference. In this paper, we develop a novel pathological image retrieval method for breast cancer, which is based on stain component and probabilistic latent semantic analysis (pLSA) model. Specifically, the method firstly utilizes color deconvolution to gain the representation of different stain components for cell nuclei and cytoplasm, and then block Gabor features are conducted on cell nuclei, which is used to construct the codebook. Furthermore, the connection between the words of the codebook and the latent topics among images are modeled by pLSA. Therefore, each image can be represented by the topics and also the high-level semantic concepts of image can be described. Experiments on the pathological image database for breast cancer demonstrate the effectiveness of our method.
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Yibing Ma, Yibing Ma, Jun Shi, Jun Shi, Zhiguo Jiang, Zhiguo Jiang, Hao Feng, Hao Feng, } "PLSA-based pathological image retrieval for breast cancer with color deconvolution", Proc. SPIE 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing, 89200L (27 October 2013); doi: 10.1117/12.2032054; https://doi.org/10.1117/12.2032054
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