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

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

7 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルMedical Imaging 2017
ホスト出版物のサブタイトルDigital Pathology
編集者Metin N. Gurcan, John E. Tomaszewski
出版社SPIE
ISBN(電子版)9781510607255
DOI
出版ステータス出版済み - 2017
イベントMedical Imaging 2017: Digital Pathology - Orlando, 米国
継続期間: 2月 12 20172月 13 2017

出版物シリーズ

名前Progress in Biomedical Optics and Imaging - Proceedings of SPIE
10140
ISSN(印刷版)1605-7422

その他

その他Medical Imaging 2017: Digital Pathology
国/地域米国
CityOrlando
Period2/12/172/13/17

!!!All Science Journal Classification (ASJC) codes

  • 電子材料、光学材料、および磁性材料
  • 生体材料
  • 原子分子物理学および光学
  • 放射線学、核医学およびイメージング

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