-
- 國枝 琢也
- 岐阜大学大学院医学系研究科知能イメージ情報分野
-
- 内山 良一
- 岐阜大学大学院医学系研究科医療情報学分野
-
- 原 武史
- 岐阜大学大学院医学系研究科知能イメージ情報分野
-
- 藤田 広志
- 岐阜大学大学院医学系研究科知能イメージ情報分野
-
- 加藤 博基
- 岐阜大学大学院医学系研究科放射線医学分野
-
- 浅野 隆彦
- 岐阜大学大学院医学系研究科放射線医学分野
-
- 兼松 雅之
- 岐阜大学大学院医学系研究科放射線医学分野
-
- 星 博昭
- 岐阜大学大学院医学系研究科放射線医学分野
-
- 岩間 亨
- 岐阜大学大学院医学系研究科脳神経外科学分野
-
- 紀ノ定 保臣
- 岐阜大学大学院医学系研究科医療情報学分野
-
- 横山 和俊
- 木沢記念病院脳神経外科
-
- 篠田 淳
- 木沢記念病院脳神経外科
書誌事項
- タイトル別名
-
- Computerized Classification of Lacunar Infarcts and Enlarged Virchow-Robin Spaces in Brain MR Images
この論文をさがす
抄録
The detection of asymptomatic lacunar infarcts on magnetic resonance (MR) images is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification of lacunar infarcts on MR images is often hard for radiologists because of the difficulty in distinguishing lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided diagnosis (CAD) scheme for the classification of lacunar infarcts and enlarged Virchow-Robin spaces. Our database consisted of T1-and T2-weighted images obtained from 52 patients, which included 89 lacunar infarcts and 20 enlarged Virchow-Robin spaces. The locations of lacunar infarcts and enlarged Virchow-Robin spaces were determined by experienced neuroradiologists. We first enhanced the lesions in T2-weighted image by using the white top-hat transformation. A gray-level thresholding was then applied to the enhanced image for the segmentation of lesions. From the segmented lesions, we determined image features, such as size, shape, location, and signal intensities in T1-and T2-weighted images. A neural network was then employed for distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Our computerized method was evaluated by using a leave-one-out method. The result indicated that the area under the ROC curve was 0.893. Therefore, our CAD scheme would be useful in assisting radiologists for distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces in MR images.
収録刊行物
-
- 医用画像情報学会雑誌
-
医用画像情報学会雑誌 26 (3), 59-63, 2009
医用画像情報学会
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390282679630761728
-
- NII論文ID
- 130000138571
-
- NII書誌ID
- AN10156808
-
- ISSN
- 18804977
- 09101543
-
- HANDLE
- 20.500.12099/47230
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- IRDB
- CiNii Articles
- KAKEN
-
- 抄録ライセンスフラグ
- 使用不可