Medical Image Segmentation and Detection Based on Reinforcement Learning

DOI 14 References
  • USUI Yukiya
    Course of Electronic System Engineering, Gifu National College of Technology
  • TSAI Du-Yih
    Department of Radiological Technology, School of Health Sciences, Niigata University
  • KOJIMA Katsuyuki
    Faculty of Administration and Informatics, University of Hamamatsu
  • YAMADA Isao
    Course of Electronic System Engineering, Gifu National College of Technology

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Other Title
  • 強化学習法に基づく医用画像のセグメンテーションおよび関心領域の抽出

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Description

Reinforcement learning (RL) is an approach to machine intelligence. It combines the fields of dynamic programming and supervised learning to yield powerful machine-learning systems. The RL appeals to many researchers because of its generality. However, it has not been used yet in the field of image processing. In RL, the computer is simply given a goal to achieve. The computer then learns how to achieve that goal by trial-and-error interactions with its environment. Of the RL methods Q-learning is a typical learning approach. In this paper, we present a novel method for image segmentation based on the Q-learning. Additionally, we illustrate the proposed algorithm and demonstrate its effectiveness for image contour extraction and region-of-interest detection using three medical images. Our preliminary results are promising.

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Details 詳細情報について

  • CRID
    1390001205456447104
  • NII Article ID
    10010439533
  • NII Book ID
    AN10156808
  • DOI
    10.11318/mii1984.17.72
  • ISSN
    09101543
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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