Computational grading of hepatocellular carcinoma using multifractal feature description

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

研究成果: Article査読

25 被引用数 (Scopus)


Cancer grading has become an important topic in the field of image interpretation-based computer aided diagnosis systems. This paper proposes a novel feature descriptor to observe the characteristics of histopathological textures in a discriminative manner. The proposed feature descriptor utilizes fractal geometric analysis with four multifractal measures to construct an eight dimensional feature space. The proposed method employed a bag-of-feature-based classification model to discriminate a set of hepatocellular carcinoma images into five categories according to Edmondson and Steiner's grading system. Three feature selection methods were utilized to obtain the most discriminative features of codeword dictionary (codebook). Furthermore, we incorporated four other textural feature descriptors: Gabor-filters, LM-filters, local binary patterns, and Haralick, to obtain a benchmark of the accuracy of the classification. Two experiments were performed: (i) classifying non-neoplastic tissues and tumors and (ii) grading the hepatocellular carcinoma images into five classes. Experimental results indicated the significance of the multifractal features for describing the histopathological image texture because it outperformed other four feature descriptors. We graded a given ROI image by defining a threshold-based majority-voting rule and obtained an average correct classification rate around 95% for five classes classification.

ジャーナルComputerized Medical Imaging and Graphics
出版ステータスPublished - 2013 1

ASJC Scopus subject areas

  • 放射線技術および超音波技術
  • 放射線学、核医学およびイメージング
  • コンピュータ ビジョンおよびパターン認識
  • 健康情報学
  • コンピュータ グラフィックスおよびコンピュータ支援設計


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