Pathology Image Retrieval by Block LBP Based pLSA Model with Low-Rank and Sparse Matrix Decomposition
Content-based image retrieval (CBIR) is widely used in Computer Aided Diagnosis (CAD) systems which can aid pathologist to make reasonable decision by effectively querying the slides with diagnostic information from the digital pathology slide database. In this paper, we propose a novel pathology image retrieval method for breast cancer. It firstly applies block Local Binary Pattern (LBP) features to describe the spatial texture property of pathology image, and then use them to construct the probabilistic Latent Semantic Analysis (pLSA) model which generally takes advantage of visual words to mine the topic-level representation of image and thus reveals the high-level semantics. Different from conventional pLSA model, we employ low-rank and sparse matrix decomposition for describing the correlated and specific characteristics of visual words. Therefore. the more discriminative topic-level representation corresponding to each pathology image can be obtained, Experimental results on the digital pathology image database for breast cancer demonstrate the feasibility and effectiveness of our method.
Image retrieval computer aided diagnosis breast cancer probabilistic latent semantic analysis low-rank and sparse matrix decomposition
Yushan Zheng Zhiguo Jiang Jun Shi Yibing Ma
Image Processing Center, School of Astronautics.Beihang University Beijing Key Laboratory of Distal Media Beijing, China
国际会议
9th Conference on Image and Graphics Technologies and Applications(IGTA2014)(第九届图像图形技术与应用学术会议)
北京
英文
343-352
2014-06-01（万方平台首次上网日期，不代表论文的发表时间）