27 April 2015 An accurate method of extracting fat droplets in liver images for quantitative evaluation
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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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Masahiro Ishikawa, Masahiro Ishikawa, Naoki Kobayashi, Naoki Kobayashi, Hideki Komagata, Hideki Komagata, Kazuma Shinoda, Kazuma Shinoda, Masahiro Yamaguchi, Masahiro Yamaguchi, Tokiya Abe, Tokiya Abe, Akinori Hashiguchi, Akinori Hashiguchi, Michiie Sakamoto, Michiie Sakamoto, "An accurate method of extracting fat droplets in liver images for quantitative evaluation", Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200Y (27 April 2015); doi: 10.1117/12.2081670; https://doi.org/10.1117/12.2081670

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