Journal of Pathology Informatics Journal of Pathology Informatics
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SYMPOSIUM INTERNATIONAL ACADEMY OF DIGITAL PATHOLOGY (IADP)
Year : 2015  |  Volume : 6  |  Issue : 1  |  Page : 26

Enhancing automatic classification of hepatocellular carcinoma images through image masking, tissue changes and trabecular features


1 Department of Information Processing, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259-S1-17 Nagatsuta, Midori, Yokohama, Kanagawa 226-8503, Japan
2 Global Scientific Information and Computing Center, Tokyo Institute of Technology, 2-12-1-I7-6 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
3 Department of Information Processing, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259-S1-17 Nagatsuta, Midori, Yokohama, Kanagawa 226-8503; Global Scientific Information and Computing Center, Tokyo Institute of Technology, 2-12-1-I7-6 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
4 Medical Solutions Division, NEC Corporation, 5-7-1 Shiba Minato-ku, Tokyo 108-8001, Japan
5 Department of Information Processing, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259-S1-17 Nagatsuta, Midori, Yokohama, Kanagawa 226-8503; Medical Solutions Division, NEC Corporation, 5-7-1 Shiba Minato-ku, Tokyo 108-8001, Japan
6 Faculty of Health and Medical Care, Saitama Medical University, 1397-1 Yamane, Hidaka-shi, Saitama 350-1241, Japan
7 Department of Pathology, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan

Correspondence Address:
Maulana Abdul Aziz
Department of Information Processing, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259-S1-17 Nagatsuta, Midori, Yokohama, Kanagawa 226-8503
Japan
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.158044

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Background: Recent breakthroughs in computer vision and digital microscopy have prompted the application of such technologies in cancer diagnosis, especially in histopathological image analysis. Earlier, an attempt to classify hepatocellular carcinoma images based on nuclear and structural features has been carried out on a set of surgical resected samples. Here, we proposed methods to enhance the process and improve the classification performance. Methods: First, we segmented the histological components of the liver tissues and generated several masked images. By utilizing the masked images, some set of new features were introduced, producing three sets of features consisting nuclei, trabecular and tissue changes features. Furthermore, we extended the classification process by using biopsy resected samples in addition to the surgical samples. Results: Experiments by using support vector machine (SVM) classifier with combinations of features and sample types showed that the proposed methods improve the classification rate in HCC detection for about 1-3%. Moreover, detection rate of low-grades cancer increased when the new features were appended in the classification process, although the rate was worsen in the case of undifferentiated tumors. Conclusions: The masking process increased the reliability of extracted nuclei features. The additional of new features improved the system especially for early HCC detection. Likewise, the combination of surgical and biopsy samples as training data could also improve the classification rates. Therefore, the methods will extend the support for pathologists in the HCC diagnosis.


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