Chamidu Atupelage, Hiroshi Nagahashi, Masahiro Yamaguchi, Fumikazu Kimura, Tokiya Abe, Akinori Hashiguchi, Michiie Sakamoto
Multifractal computation, Multifractal measures, Featuredescriptor, Cancer grading, HCC histological images.
Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper pro- poses a textural feature descriptor and a novel computa- tional method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural fea- ture 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 classi- fication 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 accu- racy.
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