An accurate method of extracting fat droplets in liver images for quantitative evaluation

Masahiro Ishikawa, Naoki Kobayashi, Hideki Komagata, Kazuma Shinoda, Masahiro Yamaguchi, Tokiya Abe, Akinori Hashiguchi, Michiie Sakamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.

Original languageEnglish
Title of host publicationMedical Imaging 2015
Subtitle of host publicationDigital Pathology
EditorsMetin N. Gurcan, Anant Madabhushi
ISBN (Electronic)9781628415100
Publication statusPublished - 2015
EventMedical Imaging 2015: Digital Pathology - Orlando, United States
Duration: 2015 Feb 252015 Feb 26

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2015: Digital Pathology
Country/TerritoryUnited States


  • Extract of fat droplets
  • Histopathological tissue images
  • Quantification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging
  • Biomaterials


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