2017 JRNL PP Yibing Ma - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
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.