Tissue classification of liver pathological tissue specimens image using spectral features

Emi Hashimoto; Masahiro Ishikawa; Kazuma Shinoda; Madoka Hasegawa; Hideki Komagata; Naoki Kobayashi; Naoki Mochidome; Yoshinao Oda; Chika Iwamoto; Kenoki Ohuchida; Makoto Hashizume

doi:10.1117/12.2253818

1 March 2017 Tissue classification of liver pathological tissue specimens image using spectral features

Emi Hashimoto, Masahiro Ishikawa, Kazuma Shinoda, Madoka Hasegawa, Hideki Komagata, Naoki Kobayashi, Naoki Mochidome, Yoshinao Oda, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume

Author Affiliations +

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400Z (2017) https://doi.org/10.1117/12.2253818
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

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, Masahiro Ishikawa, Kazuma Shinoda, Madoka Hasegawa, Hideki Komagata, Naoki Kobayashi, Naoki Mochidome, Yoshinao Oda, Chika Iwamoto, Kenoki Ohuchida, and 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); https://doi.org/10.1117/12.2253818

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