テンプレートマッチングを用いたラクナ梗塞検出のためのコンピュータ支援診断システムの改良  [in Japanese] Improvement of CAD Scheme for Detection of Lacunar Infarcts on MR images By Using Template Matching  [in Japanese]

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Author(s)

    • 阿部 晃子 ABE Akiko
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 内山 良一 UCHIYAMA Yoshikazu
    • 熊本大学大学院生命科学研究部先端生命医療科学部門 Department of Medical Physics, Faculty of Life Science, Kumamoto University
    • 村松 千左子 MURAMATSU Chisako
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 原 武史 HARA Takeshi
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 白石 順二 SHIRAISHI Junji
    • 熊本大学大学院生命科学研究部先端生命医療科学部門 Department of Medical Physics, Faculty of Life Science, Kumamoto University
    • 藤田 広志 FUJITA Hiroshi
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University

Abstract

The detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction. However, their accurate identification is often hard because of the difficulty in distinguishing between lacunar infarcts and Virchow-Robin spaces. The purpose of this study is to improve our CAD scheme by using template matching for reduction of false positives (FPs) . Our database comprised 1,143 T1- and 1,143 T2-weighted images obtained from 132 patients. The proposed method was evaluated by using 2 hold cross validation method. As a result, 17.1% of FPs were eliminated more by adding the template matching to our previous CAD scheme. Conclusively, the sensitivity of the detection of lacunar infarcts was 96.8% (90/93) with 0.59 FP per slice image. Our improved CAD scheme would be useful in assisting radiologists for identifying lacunar infarcts in MR images.

Journal

  • Medical Imaging and Information Sciences

    Medical Imaging and Information Sciences 30(2), 39-43, 2013

    MEDICAL IMAGING AND INFORMATION SCIENCES

Codes

  • NII Article ID (NAID)
    130003366635
  • Text Lang
    JPN
  • ISSN
    0910-1543
  • Data Source
    J-STAGE 
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