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Enhancing Automatic Classification of Hepatocellular Carcinoma Images through Image Masking, Tissue Changes, and Trabecular Features
Joint Authors
Abe, Tokiya
Yamashita, Yoshiko
Kiyuna, Tomoharu
Hashiguchi, Akinori
Abdul Aziz, Maulana
Kanazawa, Hiroshi
Murakami, Yuri
Kimura, Fumikazu
Yamaguchi, Masahiro
Sakamoto, Michiie
Saito, Akira
Ishikawa, Masahiro
Kobayashi, Naoki
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-2, 2 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-12-17
Country of Publication
Egypt
No. of Pages
2
Main Subjects
Abstract EN
Hepatocellular carcinoma (HCC) is a malignant tumor with hepatocellular differentiation and one of the most common cancers in the world.
This type of cancer is often diagnosed when the survival time is measured in months causing high death rates [1].
For the purpose of supporting histopathology diagnosis of HCC, we have developed an experimental system of “feature measurement software for liver biopsy” [2].
The system provides pathologists with the quantitative measurement of tissue morphology using a digital slide of hematoxylin-eosin (HE) stained liver tissue specimen, as well as the HCC detection based on those measurement results.
In this study, we are focusing on the classification process of HCC images in the system.
Previously, Kiyuna et al.
[3] had introduced an automatic classification of HCC images based on 13 types of nuclear and structural features, where each feature consists of 6 statistical distributions.
In order to improve the classification performance, we have developed methods to segment the liver tissue and quantify additional tissue features such as trabecular morphology [4].
This paper reports the evaluation results on the impact of the segmentation and the additional features in the HCC detection performance.
American Psychological Association (APA)
Abdul Aziz, Maulana& Kanazawa, Hiroshi& Murakami, Yuri& Kimura, Fumikazu& Yamaguchi, Masahiro& Kiyuna, Tomoharu…[et al.]. 2014. Enhancing Automatic Classification of Hepatocellular Carcinoma Images through Image Masking, Tissue Changes, and Trabecular Features. Analytical Cellular Pathology،Vol. 2014, no. 2014, pp.1-2.
https://search.emarefa.net/detail/BIM-1034184
Modern Language Association (MLA)
Abdul Aziz, Maulana…[et al.]. Enhancing Automatic Classification of Hepatocellular Carcinoma Images through Image Masking, Tissue Changes, and Trabecular Features. Analytical Cellular Pathology No. 2014 (2014), pp.1-2.
https://search.emarefa.net/detail/BIM-1034184
American Medical Association (AMA)
Abdul Aziz, Maulana& Kanazawa, Hiroshi& Murakami, Yuri& Kimura, Fumikazu& Yamaguchi, Masahiro& Kiyuna, Tomoharu…[et al.]. Enhancing Automatic Classification of Hepatocellular Carcinoma Images through Image Masking, Tissue Changes, and Trabecular Features. Analytical Cellular Pathology. 2014. Vol. 2014, no. 2014, pp.1-2.
https://search.emarefa.net/detail/BIM-1034184
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034184