Improvement of Automatic Detection Method of Lacunar Infarcts on MR Images: Reduction of False Positives By Using AdaBoost Template Matching

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  • TANIGAWA Ayaka
    Department of Information Science, Faculty of Engineering, Gifu University
  • UCHIYAMA Yoshikazu
    Department of Medical Physics, Faculty of Life Science, Kumamoto University
  • MURAMATSU Chisako
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
  • HARA Takeshi
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
  • SHIRAISHI Junji
    Department of Medical Physics, Faculty of Life Science, Kumamoto University
  • FUJITA Hiroshi
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University

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  • 脳MR画像におけるラクナ梗塞の検出法の改良－AdaBoostテンプレートマッチングを用いた偽陽性削除－

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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 enlarged Virchow-Robin spaces. Therefore, we developed computer-aided diagnosis scheme for the detection of lacunar infarcts. The performance of our previous method indicated that the sensitivity of 96.8% with 0.76 false positive（FP）per slice. However, further reduction of FPs was remained as an issue to be solved for the clinical application. In this paper, we proposed AdaBoost template matching. This classifier can distinguish between lacunar infarcts and FPs by selecting suitable templates in the template matching. By using this technique, 55.5% FPs were eliminated while keeping the same sensitivity. Thus the proposed method was found to be useful for the sophistication of the automatic detection of lacunar infarcts in MR images.

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