Multifractal Computation for Nuclear Classification and Hepatocellular Carcinoma Grading
Chamidu Atupelage, Hiroshi Nagahashi, Masahiro Yamaguchi, Fumikazu Kimura, Tokiya Abe, Akinori Hashiguchi, Michiie Sakamoto · 2013
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 characteristic s of the texture patterns by using multifractal computation. The textural features are utilized for nuclear segmentation, fi ber region detection, and liver cell nuclei classification. Fou r 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 liv er cell nuclei were utilized for grading a set of HCC images into four classes and obtained 97.77% classification accu- racy.