@inproceedings{101f5f4e40914af496ee7ba49affefb7,
title = "Tissue classification of liver pathological tissue specimens image using spectral features",
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.",
author = "Emi Hashimoto and Masahiro Ishikawa and Kazuma Shinoda and Madoka Hasegawa and Hideki Komagata and Naoki Kobayashi and Naoki Mochidome and Yoshinao Oda and Chika Iwamoto and Kenoki Ohuchida and Makoto Hashizume",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Digital Pathology ; Conference date: 12-02-2017 Through 13-02-2017",
year = "2017",
doi = "10.1117/12.2253818",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Gurcan, {Metin N.} and Tomaszewski, {John E.}",
booktitle = "Medical Imaging 2017",
address = "United States",
}