Multifractal computation for nuclear classification and hepatocellular carcinoma grading

Chamidu Atupelage; Hiroshi Nagahashi; Masahiro Yamaguchi; Fumikazu Kimura; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

doi:10.2316/P.2013.791-127

Multifractal computation for nuclear classification and hepatocellular carcinoma grading

Chamidu Atupelage, Hiroshi Nagahashi, Masahiro Yamaguchi, Fumikazu Kimura, Tokiya Abe, Akinori Hashiguchi, Michiie Sakamoto

Department of Pathology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper proposes a textural feature descriptor and a novel computational method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural feature descriptor observes local and spatial characteristics of the texture patterns by using multifractal computation. The textural features are utilized for nuclear segmentation, fiber region detection, and liver cell nuclei classification. Four categories of nuclear features are computed such as texture, geometry, spatial distribution, and surrounding texture, for HCC classification. Significance of liver cell nuclei classification method is evaluated by classifying non-neoplastic and tumor tissues. Furthermore, characteristics of the liver cell nuclei were utilized for grading a set of HCC images into four classes and obtained 97.77% classification accuracy.

Original language	English
Title of host publication	Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013
Pages	415-420
Number of pages	6
DOIs	https://doi.org/10.2316/P.2013.791-127
Publication status	Published - 2013
Event	10th IASTED International Conference on Biomedical Engineering, BioMed 2013 - Innsbruck, Austria Duration: 2013 Feb 13 → 2013 Feb 15

Publication series

Name	Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013

Other

Other	10th IASTED International Conference on Biomedical Engineering, BioMed 2013
Country/Territory	Austria
City	Innsbruck
Period	13/2/13 → 13/2/15

Keywords

Cancer grading
Feature descriptor
HCC histological images
Multifractal computation
Multifractal measures

ASJC Scopus subject areas

Biomedical Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.2316/P.2013.791-127

Cite this

Atupelage, C., Nagahashi, H., Yamaguchi, M., Kimura, F., Abe, T., Hashiguchi, A., & Sakamoto, M. (2013). Multifractal computation for nuclear classification and hepatocellular carcinoma grading. In Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013 (pp. 415-420). (Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013). https://doi.org/10.2316/P.2013.791-127

Multifractal computation for nuclear classification and hepatocellular carcinoma grading. / Atupelage, Chamidu; Nagahashi, Hiroshi; Yamaguchi, Masahiro et al.
Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013. 2013. p. 415-420 (Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Atupelage, C, Nagahashi, H, Yamaguchi, M, Kimura, F, Abe, T, Hashiguchi, A & Sakamoto, M 2013, Multifractal computation for nuclear classification and hepatocellular carcinoma grading. in Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013. Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013, pp. 415-420, 10th IASTED International Conference on Biomedical Engineering, BioMed 2013, Innsbruck, Austria, 13/2/13. https://doi.org/10.2316/P.2013.791-127

Atupelage C, Nagahashi H, Yamaguchi M, Kimura F, Abe T, Hashiguchi A et al. Multifractal computation for nuclear classification and hepatocellular carcinoma grading. In Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013. 2013. p. 415-420. (Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013). doi: 10.2316/P.2013.791-127

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title = "Multifractal computation for nuclear classification and hepatocellular carcinoma grading",

abstract = "Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper proposes a textural feature descriptor and a novel computational method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural feature descriptor observes local and spatial characteristics of the texture patterns by using multifractal computation. The textural features are utilized for nuclear segmentation, fiber region detection, and liver cell nuclei classification. Four categories of nuclear features are computed such as texture, geometry, spatial distribution, and surrounding texture, for HCC classification. Significance of liver cell nuclei classification method is evaluated by classifying non-neoplastic and tumor tissues. Furthermore, characteristics of the liver cell nuclei were utilized for grading a set of HCC images into four classes and obtained 97.77% classification accuracy.",

keywords = "Cancer grading, Feature descriptor, HCC histological images, Multifractal computation, Multifractal measures",

author = "Chamidu Atupelage and Hiroshi Nagahashi and Masahiro Yamaguchi and Fumikazu Kimura and Tokiya Abe and Akinori Hashiguchi and Michiie Sakamoto",

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AU - Atupelage, Chamidu

AU - Nagahashi, Hiroshi

AU - Yamaguchi, Masahiro

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AU - Abe, Tokiya

AU - Hashiguchi, Akinori

AU - Sakamoto, Michiie

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N2 - Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper proposes a textural feature descriptor and a novel computational method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural feature descriptor observes local and spatial characteristics of the texture patterns by using multifractal computation. The textural features are utilized for nuclear segmentation, fiber region detection, and liver cell nuclei classification. Four categories of nuclear features are computed such as texture, geometry, spatial distribution, and surrounding texture, for HCC classification. Significance of liver cell nuclei classification method is evaluated by classifying non-neoplastic and tumor tissues. Furthermore, characteristics of the liver cell nuclei were utilized for grading a set of HCC images into four classes and obtained 97.77% classification accuracy.

AB - Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper proposes a textural feature descriptor and a novel computational method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural feature descriptor observes local and spatial characteristics of the texture patterns by using multifractal computation. The textural features are utilized for nuclear segmentation, fiber region detection, and liver cell nuclei classification. Four categories of nuclear features are computed such as texture, geometry, spatial distribution, and surrounding texture, for HCC classification. Significance of liver cell nuclei classification method is evaluated by classifying non-neoplastic and tumor tissues. Furthermore, characteristics of the liver cell nuclei were utilized for grading a set of HCC images into four classes and obtained 97.77% classification accuracy.

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KW - Multifractal measures

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Multifractal computation for nuclear classification and hepatocellular carcinoma grading

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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