title: Multifractal feature descriptor for histopathology.
abstract: BACKGROUND
  Histologic image analysis plays an important role in cancer diagnosis. It describes the structure of the body tissues and abnormal structure gives the suspicion of the cancer or some other diseases. Observing the structural changes of these chaotic textures from the human eye is challenging process. However, the challenge can be defeat by forming mathematical descriptor to represent the histologic texture and classify the structural changes via a sophisticated computational method.
  
  
  OBJECTIVE
  In this paper, we propose a texture descriptor to observe the histologic texture into highly discriminative feature space.
  
  
  METHODS
  Fractal dimension describes the self-similar structures in different and more accurate manner than topological dimension. Further, the fractal phenomenon has been extended to natural structures (images) as multifractal dimension. We exploited the multifractal analysis to represent the histologic texture, which derive more discriminative feature space for classification.
  
  
  RESULTS
  We utilized a set of histologic images (belongs to liver and prostate specimens) to assess the discriminative power of the multifractal features. The experiment was organized to classify the given histologic texture as cancer and non-cancer. The results show the discrimination capability of multifractal features by achieving approximately 95% of correct classification rate.
  
  
  CONCLUSIONS
  Multifractal features are more effective to describe the histologic texture. The proposed feature descriptor showed high classification rate for both liver and prostate data sample datasets.
authors.affiliation: Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Tokyo, Japan. atupelage.c.aa@m.titech.ac.jp
authors.forename: Chamidu
authors.initials: C
authors.lastname: Atupelage
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Nagahashi
authors.forename: Masahiro
authors.initials: M
authors.lastname: Yamaguchi
authors.forename: Michiie
authors.initials: M
authors.lastname: Sakamoto
authors.forename: Akinori
authors.initials: A
authors.lastname: Hashiguchi
mesh_terms: D000465:Algorithms; D017709:Fractals; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009369:Neoplasms / Q000145:classification* / Q000175:diagnosis*