| Computer-aided diagnosis scheme for detection of lacunar infarcts on MR images. | |
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MedLine Citation:
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PMID: 18035284 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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RATIONALE AND OBJECTIVES: The detection and management of asymptomatic lacunar infarcts on magnetic resonance (MR) images are important tasks for radiologists to ensure the prevention of severe cerebral infarctions. However, accurate identification of the lacunar infarcts on MR images is a difficult task for the radiologists. Therefore the purpose of this study was to develop a computer-aided diagnosis scheme for the detection of lacunar infarcts to assist radiologists' interpretation as a "second opinion." MATERIALS AND METHODS: Our database comprised 1,143 T1- and 1,143 T2-weighted images obtained from 132 patients. The locations of the lacunar infarcts were determined by experienced neuroradiologists. We first segmented the cerebral region in a T1-weighted image by using a region growing technique for restricting the search area of lacunar infarcts. For identifying the initial lacunar infarcts candidates, a top-hat transform and multiple-phase binarization were then applied to the T2-weighted image within the segmented cerebral region. For eliminating the false positives (FPs), we determined 12 features--the locations x and y, signal intensity differences in the T1- and T2-weighted images, nodular components from a scale of 1 to 4, and nodular and linear components from a scale of 1 to 4. The nodular components and the linear components were obtained using a filter bank technique. The rule-based schemes and a support vector machine with 12 features were applied to the regions of the initial candidates for distinguishing between lacunar infarcts and FPs. RESULTS: Our computerized scheme was evaluated by using a holdout method. The sensitivity of the detection of lacunar infarcts was 96.8% (90/93) with 0.76 FP per image. CONCLUSIONS: Our computerized scheme would be useful in assisting radiologists for identifying lacunar infarcts in MR images. |
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Authors:
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Yoshikazu Uchiyama; Ryujiro Yokoyama; Hiromich Ando; Takahiko Asano; Hiroki Kato; Hiroyasu Yamakawa; Haruki Yamakawa; Takeshi Hara; Toru Iwama; Hiroaki Hoshi; Hiroshi Fujita |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Academic radiology Volume: 14 ISSN: 1076-6332 ISO Abbreviation: Acad Radiol Publication Date: 2007 Dec |
Date Detail:
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Created Date: 2007-11-23 Completed Date: 2008-02-26 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9440159 Medline TA: Acad Radiol Country: United States |
Other Details:
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Languages: eng Pagination: 1554-61 Citation Subset: IM |
Affiliation:
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Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, Japan. uchiyama@fjt.info.gifu-u.ac.jp |
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| MeSH Terms | |
Descriptor/Qualifier:
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Adult Aged Aged, 80 and over Brain Infarction / diagnosis* Cerebral Arteries / pathology Diagnosis, Computer-Assisted / methods* False Positive Reactions Humans Image Enhancement / methods Image Interpretation, Computer-Assisted / methods Image Processing, Computer-Assisted / methods Lateral Ventricles / pathology Magnetic Resonance Imaging / methods* Middle Aged Radiology Information Systems |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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