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 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 composition 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.