書誌事項
- タイトル別名
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- Kernel Eigenspace Template Matching for Detection of Lacunar Infarcts on MR Images
- ラクナ コウソク ケンシュツ ノ タメ ノ カーネル コユウ クウカン テンプレートマッチング
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抄録
Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction and dementia. However, accurate identification of lacunar infarcts is often difficult for radiologists. Our previous computer-aided detection (CAD) scheme achieved a sensitivity of 96.8% with 0.76 false positives (FPs) per slice. However, further reduction of FPs remained an issue for the clinical application. The purpose of this study is to improve our CAD scheme by using kernel eigenspace template matching. First, we selected the regions of interest (ROIs) around the candidate regions detected in our previous method. A kernel eigenspace was then made by using kernel principal component analysis of the training data set. A test ROI was projected onto the same kernel eigenspace as the training data set. The cross-correlation coefficients between the test ROI and all the training ROIs were calculated on the kernel eigenspace. By comparing the two maxima of coefficients with a lacunar ROI and an FP ROI, the test ROI was classified. By using the proposed method, the quantity of the templates became 1.9% of that in template matching on the real space and 31. 9% of FPs could be eliminated while keeping the same sensitivity; nevertheless 30.3% of FPs were eliminated when we employed the eigenspace template matching under the same condition. Therefore, kernel eigenspace template matching could improve FP rate without a significant reduction in the true positive rate.
収録刊行物
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- 日本放射線技術学会雑誌
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日本放射線技術学会雑誌 71 (2), 85-91, 2015
公益社団法人 日本放射線技術学会
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詳細情報 詳細情報について
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- CRID
- 1390001206362967424
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- NII論文ID
- 130004776322
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- NII書誌ID
- AN00197784
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- ISSN
- 18814883
- 03694305
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- NDL書誌ID
- 026239693
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- PubMed
- 25748008
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- PubMed
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可
