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Eigenspace Template Matching for Detection of Lacunar Infarcts on MR Images

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Abstract

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification is often hindered by the difficulty in distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided detection (CAD) scheme for the detection of lacunar infarcts. Although our previous CAD method indicated a sensitivity of 96.8 % with 0.71 false positives (FPs) per slice, further reduction of FPs remained an issue for the clinical application. Thus, the purpose of this study is to improve our CAD scheme by using template matching in the eigenspace. Conventional template matching is useful for the reduction of FPs, but it has the following two pitfalls: (1) It needs to maintain a large number of templates to improve the detection performance, and (2) calculation of the cross-correlation coefficient with these templates is time consuming. To solve these problems, we used template matching in the lower dimension space made by a principal component analysis. Our database comprised 1,143 T1- and T2-weighted images obtained from 132 patients. The proposed method was evaluated by using twofold cross-validation. By using this method, 34.1 % of FPs was eliminated compared with our previous method. The final performance indicated that the sensitivity of the detection of lacunar infarcts was 96.8 % with 0.47 FPs per slice. Therefore, the modified CAD scheme could improve FP rate without a significant reduction in the true positive rate.

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Acknowledgments

This work was partly supported by JSPS KAKENHI Grant Number 24591815, and a Grant-in-Aid for Scientific Research on Innovative Areas 21103001 from the Ministry of Education, Culture, Sports, Science, and Technology, Japan.

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Authors and Affiliations

  1. Department of Medical Physics, Faculty of Life Science, Kumamoto University, 4-24-1 Kuhonji, Kumamoto, Kumamoto, 862-0976, Japan

    Yoshikazu Uchiyama & Junji Shiraishi

  2. Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1, Yanagido, Gifu, Gifu, 501-1194, Japan

    Akiko Abe, Chisako Muramatsu, Takeshi Hara & Hiroshi Fujita

Authors
  1. Yoshikazu Uchiyama
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  2. Akiko Abe
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  3. Chisako Muramatsu
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  4. Takeshi Hara
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  5. Junji Shiraishi
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  6. Hiroshi Fujita
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Correspondence to Yoshikazu Uchiyama.

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Uchiyama, Y., Abe, A., Muramatsu, C. et al. Eigenspace Template Matching for Detection of Lacunar Infarcts on MR Images. J Digit Imaging 28, 116–122 (2015). https://doi.org/10.1007/s10278-014-9711-2

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  • DOI: https://doi.org/10.1007/s10278-014-9711-2

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