title: Computational hepatocellular carcinoma tumor grading based on cell nuclei classification.
abstract: Hepatocellular carcinoma (HCC) is the most common histological type of primary liver cancer. HCC is graded according to the malignancy of the tissues. It is important to diagnose low-grade HCC tumors because these tissues have good prognosis. Image interpretation-based computer-aided diagnosis (CAD) systems have been developed to automate the HCC grading process. Generally, the HCC grade is determined by the characteristics of liver cell nuclei. Therefore, it is preferable that CAD systems utilize only liver cell nuclei for HCC grading. This paper proposes an automated HCC diagnosing method. In particular, it defines a pipeline-path that excludes nonliver cell nuclei in two consequent pipeline-modules and utilizes the liver cell nuclear features for HCC grading. The significance of excluding the nonliver cell nuclei for HCC grading is experimentally evaluated. Four categories of liver cell nuclear features were utilized for classifying the HCC tumors. Results indicated that nuclear texture is the dominant feature for HCC grading and others contribute to increase the classification accuracy. The proposed method was employed to classify a set of regions of interest selected from HCC whole slide images into five classes and resulted in a 95.97% correct classification rate.
authors.affiliation: Tokyo Institute of Technology , Imaging Science and Engineering Laboratory, 4259-R2-51, Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan.
authors.forename: Chamidu
authors.initials: C
authors.lastname: Atupelage
authors.affiliation: Tokyo Institute of Technology , Imaging Science and Engineering Laboratory, 4259-R2-51, Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Nagahashi
authors.affiliation: Tokyo Institute of Technology , Global Scientific Information and Computing Center, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
authors.forename: Fumikazu
authors.initials: F
authors.lastname: Kimura
authors.affiliation: Tokyo Institute of Technology , Global Scientific Information and Computing Center, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
authors.forename: Masahiro
authors.initials: M
authors.lastname: Yamaguchi
authors.affiliation: Keio University , Department of Pathology, School of Medicine, 35 Shinanomachi, Shinjyuku, Tokyo 160-8582, Japan.
authors.forename: Abe
authors.initials: A
authors.lastname: Tokiya
authors.affiliation: Keio University , Department of Pathology, School of Medicine, 35 Shinanomachi, Shinjyuku, Tokyo 160-8582, Japan.
authors.forename: Akinori
authors.initials: A
authors.lastname: Hashiguchi
authors.affiliation: Keio University , Department of Pathology, School of Medicine, 35 Shinanomachi, Shinjyuku, Tokyo 160-8582, Japan.
authors.forename: Michiie
authors.initials: M
authors.lastname: Sakamoto
keywords: cancer grading; classification; hepatocellular carcinoma histological images; multifractal computation; multifractal measures; segmentation; textural feature descriptor