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Proceedings Paper

Automatic classification of hepatocellular carcinoma images based on nuclear and structural features
Author(s): Tomoharu Kiyuna; Akira Saito; Atsushi Marugame; Yoshiko Yamashita; Maki Ogura; Eric Cosatto; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto
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Paper Abstract

Diagnosis of hepatocellular carcinoma (HCC) on the basis of digital images is a challenging problem because, unlike gastrointestinal carcinoma, strong structural and morphological features are limited and sometimes absent from HCC images. In this study, we describe the classification of HCC images using statistical distributions of features obtained from image analysis of cell nuclei and hepatic trabeculae. Images of 130 hematoxylin-eosin (HE) stained histologic slides were captured at 20X by a slide scanner (Nanozoomer, Hamamatsu Photonics, Japan) and 1112 regions of interest (ROI) images were extracted for classification (551 negatives and 561 positives, including 113 well-differentiated positives). For a single nucleus, the following features were computed: area, perimeter, circularity, ellipticity, long and short axes of elliptic fit, contour complexity and gray level cooccurrence matrix (GLCM) texture features (angular second moment, contrast, homogeneity and entropy). In addition, distributions of nuclear density and hepatic trabecula thickness within an ROI were also extracted. To represent an ROI, statistical distributions (mean, standard deviation and percentiles) of these features were used. In total, 78 features were extracted for each ROI and a support vector machine (SVM) was trained to classify negative and positive ROIs. Experimental results using 5-fold cross validation show 90% sensitivity for an 87.8% specificity. The use of statistical distributions over a relatively large area makes the HCC classifier robust to occasional failures in the extraction of nuclear or hepatic trabecula features, thus providing stability to the system.

Paper Details

Date Published: 29 March 2013
PDF: 6 pages
Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760Y (29 March 2013); doi: 10.1117/12.2006667
Show Author Affiliations
Tomoharu Kiyuna, NEC Corp. (Japan)
Akira Saito, NEC Corp. (Japan)
Atsushi Marugame, NEC Corp. (Japan)
Yoshiko Yamashita, NEC Corp. (Japan)
Maki Ogura, NEC Corp. (Japan)
Eric Cosatto, NEC Labs. America, Inc. (United States)
Tokiya Abe, Keio Univ. (Japan)
Akinori Hashiguchi, Keio Univ. (Japan)
Michiie Sakamoto, Keio Univ. (Japan)

Published in SPIE Proceedings Vol. 8676:
Medical Imaging 2013: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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