Retrieval of pathology image for breast cancer using PLSA model based on texture and pathological features

Content-based image retrieval (CBIR) for digital pathology slides is of clinical use for breast cancer aided diagnosis. One of the largest challenges in CBIR is feature extraction. In this paper, we propose a novel pathology image retrieval method for breast cancer, which aims to characterize the pathology image content through texture and pathological features and further discover the latent high-level semantics. Specifically, the proposed method utilizes block Gabor features to describe the texture structure, and simultaneously designs nucleus-based pathological features to describe morphological characteristics of nuclei. Based on these two kinds of local feature descriptors, two codebooks are built to learn the probabilistic latent semantic analysis (pLSA) models. Consequently, each image is represented by the topics of pLSA models which can reveal the semantic concepts. Experimental results on the digital pathology image database for breast cancer demonstrate the feasibility and effectiveness of our method.