NDLI: Improvement of Automated Detection Method of Lacunar Infarcts in Brain MR Images

Content Provider	IEEE Xplore Digital Library
Author	Uchiyama, Y. Yokoyama, R. Ando, H. Asano, T. Kato, H. Yamakawa, H. Yamakawa, H. Hara, T. Iwama, T. Hoshi, H. Fujita, H.
Copyright Year	2007
Description	Author affiliation: Gifu Univ., Gifu (Uchiyama, Y.; Yokoyama, R.)
Abstract	The detection of asymptomatic lacunar infarcts on magnetic resonance (MR) images are important tasks for radiologists to ensure the prevention of severe cerebral infarction. However, their accurate identification is often difficult task. Therefore, the purpose of this study is to develop a computer- aided diagnosis scheme for the detection of lacunar infarcts. Our database consisted of 1,143 T1- and 1,143 T2-weighted images obtained from 132 patients. We first segmented the cerebral region in the Tl- weighted image by using a region growing technique. For identifying the initial lacunar infarcts candidates, white top-hat transform and multiple-phase binarization were then applied to the T2- weighted image. For eliminating false positives (FPs), we determined 12 features, i.e., the locations x and y, density differences in the Tl- and T2-weighted images, nodular components (NC), and nodular & linear components (NLC) from a scale 1 to 4. The NCs and NLCs were obtained using filter bank technique. The rule-based scheme and a neural network with 12 features were employed as the first step for eliminating FPs. The modular classifier was then used for eliminating three typical sources of FPs. As a result, the sensitivity of the detection of lacunar infarcts was 96.8% with 0.30 FP per image. Our computerized scheme would assist radiologists in identifying lacunar infarcts on MR images.
Starting Page	1599
Ending Page	1602
File Size	1140616
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4352611
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image databases Magnetic resonance imaging Magnetic resonance Diseases Aneurysm Biomedical imaging Medical diagnostic imaging Hospitals Tellurium Spatial resolution Modular classifier Lacunar infarcts Magnetic resonance imaging (MRI) Computer-Aided Diagnosis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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