期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 21, 期 4, 页码 1114-1123出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2016.2611615
关键词
Content-based image retrieval (CBIR); latent Dirichlet allocation (LDA); local statistical feature of nuclei (LSFN); whole slide image (WSI)
类别
资金
- National Natural Science Foundation of China [61371134, 61471016, 61501009]
- Project of Motic-BUAA Image Technology Research and Development Center
In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content-based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate, and fast retrieval method for a breast histopathological image. Specifically, the method presents a local statistical feature of nuclei for morphology and distribution of nuclei, and employs the Gabor feature to describe the texture information. The latent Dirichlet allocation model is utilized for high-level semantic mining. Locality-sensitive hashing is used to speed up the search. Experiments on a WSI database with more than 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, and WSI archiving and management.
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