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3 June 2016 Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens
Masahiro Ishikawa, Yuri Murakami, Sercan Taha Ahi, Masahiro Yamaguchi, Naoki Kobayashi, Tomoharu Kiyuna, Yoshiko Yamashita, Akira Saito, Tokiya Abe, Akinori Hashiguchi, Michiie Sakamoto
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Journal of Medical Imaging, Vol. 3, Issue 2, 027502 (June 2016). https://doi.org/10.1117/1.JMI.3.2.027502
Abstract
This paper proposes a digital image analysis method to support quantitative pathology by automatically segmenting the hepatocyte structure and quantifying its morphological features. To structurally analyze histopathological hepatic images, we isolate the trabeculae by extracting the sinusoids, fat droplets, and stromata. We then measure the morphological features of the extracted trabeculae, divide the image into cords, and calculate the feature values of the local cords. We propose a method of calculating the nuclear–cytoplasmic ratio, nuclear density, and number of layers using the local cords. Furthermore, we evaluate the effectiveness of the proposed method using surgical specimens. The proposed method was found to be an effective method for the quantification of the Edmondson grade.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Masahiro Ishikawa, Yuri Murakami, Sercan Taha Ahi, Masahiro Yamaguchi, Naoki Kobayashi, Tomoharu Kiyuna, Yoshiko Yamashita, Akira Saito, Tokiya Abe, Akinori Hashiguchi, and Michiie Sakamoto "Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens," Journal of Medical Imaging 3(2), 027502 (3 June 2016). https://doi.org/10.1117/1.JMI.3.2.027502
Published: 3 June 2016
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Cited by 11 scholarly publications and 1 patent.
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KEYWORDS
Tissues
Liver
Image analysis
Image segmentation
Feature extraction
Pathology
Cancer
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