期刊
MEDICAL IMAGE ANALYSIS
卷 65, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.media.2020.101788
关键词
Stain normalization; Microscopic images; Jenner-Giemsa stain; Hematoxylin-Eosin stain
类别
资金
- Ministry of Communication and IT, Govt. of India [1(7)/2014-MEHI]
- Department of Science and Technology (DST), Govt. of India [EMR/2016/006183]
- University Grant Commission (UGC), Govt. of India
Stain normalization of microscopic images is the first pre-processing step in any computer-assisted automated diagnostic tool. This paper proposes Geometry-inspired Chemical-invariant and Tissue Invariant Stain Normalization method, namely GCTI-SN, for microscopic medical images. The proposed GCTISN method corrects for illumination variation, stain chemical, and stain quantity variation in a unified framework by exploiting the underlying color vector space's geometry. While existing stain normalization methods have demonstrated their results on a single tissue and stain type, GCTI-SN is benchmarked on three cancer datasets of three cell/tissue types prepared with two different stain chemicals. GCTI-SN method is also benchmarked against the existing methods via quantitative and qualitative results, validating its robustness for stain chemical and cell/tissue type. Further, the utility and the efficacy of the proposed GCTI-SN stain normalization method is demonstrated diagnostically in the application of breast cancer detection via a CNN-based classifier. (c) 2020 Elsevier B.V. All rights reserved.
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