Computer-Aided Diagnosis

Computational hepatocellular carcinoma tumor grading based on cell nuclei classification

[+] Author Affiliations
Chamidu Atupelage

Tokyo Institute of Technology, Imaging Science and Engineering Laboratory, 4259-R2-51, Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan

Hiroshi Nagahashi

Tokyo Institute of Technology, Imaging Science and Engineering Laboratory, 4259-R2-51, Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan

Fumikazu Kimura

Tokyo Institute of Technology, Global Scientific Information and Computing Center, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan

Masahiro Yamaguchi

Tokyo Institute of Technology, Global Scientific Information and Computing Center, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan

Abe Tokiya

Keio University, Department of Pathology, School of Medicine, 35 Shinanomachi, Shinjyuku, Tokyo 160-8582, Japan

Akinori Hashiguchi

Keio University, Department of Pathology, School of Medicine, 35 Shinanomachi, Shinjyuku, Tokyo 160-8582, Japan

Michiie Sakamoto

Keio University, Department of Pathology, School of Medicine, 35 Shinanomachi, Shinjyuku, Tokyo 160-8582, Japan

J. Med. Imag. 1(3), 034501 (Oct 09, 2014). doi:10.1117/1.JMI.1.3.034501
History: Received May 14, 2014; Revised September 5, 2014; Accepted September 11, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Chamidu Atupelage ; Hiroshi Nagahashi ; Fumikazu Kimura ; Masahiro Yamaguchi ; Abe Tokiya, et al.
"Computational hepatocellular carcinoma tumor grading based on cell nuclei classification", J. Med. Imag. 1(3), 034501 (Oct 09, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.3.034501


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