NDLI: Pathological Image Retrieval for Breast Cancer with pLSA Model

Content Provider	IEEE Xplore Digital Library
Author	Jun Shi Yibing Ma Zhiguo Jiang Hao Feng Jin Chen Yu Zhao
Copyright Year	2013
Description	Author affiliation: Beijing Key Lab. of Digital Media, Beihang Univ., Beijing, China (Jun Shi; Yibing Ma; Zhiguo Jiang; Hao Feng) \|\| Motic China Group Corp., Ltd., Xiamen, China (Jin Chen; Yu Zhao)
Abstract	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.
Starting Page	634
Ending Page	638
File Size	643491
Page Count	5
File Format	PDF
ISBN	9780769550503
DOI	10.1109/ICIG.2013.131
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pathology Visualization Image retrieval Semantics Feature extraction breast cancer Breast cancer saliency pathogogical image retrieval pLSA
Content Type	Text
Resource Type	Article

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