Share Email Print

Journal of Medical Imaging

Computational hepatocellular carcinoma tumor grading based on cell nuclei classification
Author(s): Chamidu Atupelage; Hiroshi Nagahashi; Fumikazu Kimura; Masahiro Yamaguchi; Abe Tokiya; Akinori Hashiguchi; Michiie Sakamoto
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 9 October 2014
PDF: 13 pages
J. Med. Imag. 1(3) 034501 doi: 10.1117/1.JMI.1.3.034501
Published in: Journal of Medical Imaging Volume 1, Issue 3
Show Author Affiliations
Chamidu Atupelage, Tokyo Institute of Technology (Japan)
Hiroshi Nagahashi, Tokyo Institute of Technology (Japan)
Fumikazu Kimura, Tokyo Institute of Technology (Japan)
Masahiro Yamaguchi, Tokyo Institute of Technology (Japan)
Abe Tokiya, Keio Univ. School of Medicine (Japan)
Akinori Hashiguchi, Keio Univ. School of Medicine (Japan)
Michiie Sakamoto, Keio Univ. School of Medicine (Japan)

© SPIE. Terms of Use
Back to Top