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Proceedings Paper

PLSA-based pathological image retrieval for breast cancer with color deconvolution
Author(s): Yibing Ma; Jun Shi; Zhiguo Jiang; Hao Feng
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Paper 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.

Paper Details

Date Published: 27 October 2013
PDF: 7 pages
Proc. SPIE 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing, 89200L (27 October 2013); doi: 10.1117/12.2032054
Show Author Affiliations
Yibing Ma, Beihang Univ. (China)
Beijing Key Lab. of Digital Media (China)
Jun Shi, Beihang Univ. (China)
Beijing Key Lab. of Digital Media (China)
Zhiguo Jiang, Beihang Univ. (China)
Beijing Key Lab. of Digital Media (China)
Hao Feng, Beihang Univ. (China)
Beijing Key Lab. of Digital Media (China)


Published in SPIE Proceedings Vol. 8920:
MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing
Jianguo Liu, Editor(s)

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