Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

Tomoharu Kiyuna; Akira Saito; Atsushi Marugame; Yoshiko Yamashita; Maki Ogura; Eric Cosatto; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

doi:10.1117/12.2006667

29 March 2013 Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

Tomoharu Kiyuna, Akira Saito, Atsushi Marugame, Yoshiko Yamashita, Maki Ogura, Eric Cosatto, Tokiya Abe, Akinori Hashiguchi, Michiie Sakamoto

Author Affiliations +

Proceedings Volume 8676, Medical Imaging 2013: Digital Pathology; 86760Y (2013) https://doi.org/10.1117/12.2006667
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

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Tomoharu Kiyuna, Akira Saito, Atsushi Marugame, Yoshiko Yamashita, Maki Ogura, Eric Cosatto, Tokiya Abe, Akinori Hashiguchi, and Michiie Sakamoto "Automatic classification of hepatocellular carcinoma images based on nuclear and structural features", Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760Y (29 March 2013); https://doi.org/10.1117/12.2006667

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