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Proceedings Paper

Tissue classification of liver pathological tissue specimens image using spectral features
Author(s): Emi Hashimoto; Masahiro Ishikawa; Kazuma Shinoda; Madoka Hasegawa; Hideki Komagata; Naoki Kobayashi; Naoki Mochidome; Yoshinao Oda; Chika Iwamoto; Kenoki Ohuchida; Makoto Hashizume
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Paper 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.

Paper Details

Date Published: 1 March 2017
PDF: 6 pages
Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400Z (1 March 2017); doi: 10.1117/12.2253818
Show Author Affiliations
Emi Hashimoto, Utsunomiya Univ. (Japan)
Masahiro Ishikawa, Saitama Medical Univ. (Japan)
Kazuma Shinoda, Utsunomiya Univ. (Japan)
Madoka Hasegawa, Utsunomiya Univ. (Japan)
Hideki Komagata, Saitama Medical Univ. (Japan)
Naoki Kobayashi, Saitama Medical Univ. (Japan)
Naoki Mochidome, Kyushu Univ. (Japan)
Yoshinao Oda, Kyushu Univ. (Japan)
Chika Iwamoto, Kyushu Univ. (Japan)
Kenoki Ohuchida, Kyushu Univ. (Japan)
Makoto Hashizume, Kyushu Univ. (Japan)


Published in SPIE Proceedings Vol. 10140:
Medical Imaging 2017: Digital Pathology
Metin N. Gurcan; John E. Tomaszewski, Editor(s)

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