Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation | IEEE Journals & Magazine | IEEE Xplore

Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation


Abstract:

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...Show More

Abstract:

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.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 21, Issue: 4, July 2017)
Page(s): 1114 - 1123
Date of Publication: 20 September 2016

ISSN Information:

PubMed ID: 27662689

Funding Agency:


References

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