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

التفاصيل البيبلوغرافية
العنوان: Enhancing automatic classification of hepatocellular carcinoma images through image masking, tissue changes and trabecular features
المؤلفون: Maulana Abdul Aziz, Hiroshi Kanazawa, Yuri Murakami, Fumikazu Kimura, Masahiro Yamaguchi, Tomoharu Kiyuna, Yoshiko Yamashita, Akira Saito, Masahiro Ishikawa, Naoki Kobayashi, Tokiya Abe, Akinori Hashiguchi, Michiie Sakamoto
المصدر: Journal of Pathology Informatics, Vol 6, Iss 1, Pp 26-26 (2015)
بيانات النشر: Elsevier, 2015.
سنة النشر: 2015
المجموعة: LCC:Computer applications to medicine. Medical informatics
LCC:Pathology
مصطلحات موضوعية: Hepatocellular carcinoma, histopathological image classification, image masking, liver tissue, liver trabecular, support vector machine, Computer applications to medicine. Medical informatics, R858-859.7, Pathology, RB1-214
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2153-3539
Relation: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2015;volume=6;issue=1;spage=26;epage=26;aulast=Aziz; https://doaj.org/toc/2153-3539
DOI: 10.4103/2153-3539.158044
URL الوصول: https://doaj.org/article/8fc990e013194b05ad58cff4270e3f02
رقم الأكسشن: edsdoj.8fc990e013194b05ad58cff4270e3f02
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21533539
DOI:10.4103/2153-3539.158044