Pathological Image Retrieval for Breast Cancer with pLSA Model

Pathological image retrieval contributes to computer-aided diagnosis for breast cancer due to the fact that the retrieval results generally contain detailed diagnostic information (e.g. abnormal regions and diagnostic opinion from other doctors) which can offer some reference and assistance to the doctor during diagnosis process. In this paper, we present a novel pathological image retrieval approach based on probabilistic latent semantic analysis (pLSA) model. The method respectively utilizes SIFT features after visual saliency detection, and block Gabor features for the construction of two semantic codebooks, which not only can characterize the salient local invariant features and texture information under different scales and orientations in the pathological images, but also consider the high-level semantic features. Furthermore, we apply pLSA model to discover the latent topics in each codebook. Finally each pathological image is represented by the combination of topics from these two codebooks. The proposed method is evaluated on the pathological image database for breast cancer, which includes 5 categories (mucinous cystadenocarcinoma, invasive lobular carcinoma, basal-like carcinoma, invasive breast cancer and low-grade adenosquamous carcinoma) and 110 cases for each category. Experimental results demonstrate the feasibility and effectiveness of our method.