1 March 2017 Tissue classification of liver pathological tissue specimens image using spectral features
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In digital pathology diagnosis, accurate recognition and quantification of the tissue structure is an important factor for computer-aided diagnosis. However, the classification accuracy of cytoplasm is low in Hematoxylin and eosin (HE) stained liver pathology specimens because the RGB color values of cytoplasm are almost similar to that of fibers. In this paper, we propose a new tissue classification method for HE stained liver pathology specimens by using hyperspectral image. At first we select valid spectra from the image to make a clear distinction between fibers and cytoplasm, and then classify five types of tissue based on the bag of features (BoF). The average classification accuracy for all tissues was improved by 11% in the case of using BoF of RGB and selected spectra bands in comparison with using only RGB. In particular, the improvement reached to 24% for fibers and 5% for cytoplasm.
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Emi Hashimoto, Emi Hashimoto, Masahiro Ishikawa, Masahiro Ishikawa, Kazuma Shinoda, Kazuma Shinoda, Madoka Hasegawa, Madoka Hasegawa, Hideki Komagata, Hideki Komagata, Naoki Kobayashi, Naoki Kobayashi, Naoki Mochidome, Naoki Mochidome, Yoshinao Oda, Yoshinao Oda, Chika Iwamoto, Chika Iwamoto, Kenoki Ohuchida, Kenoki Ohuchida, Makoto Hashizume, Makoto Hashizume, } "Tissue classification of liver pathological tissue specimens image using spectral features", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400Z (1 March 2017); doi: 10.1117/12.2253818; https://doi.org/10.1117/12.2253818

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