Presentation 2007-01-26
Computer-aided Diagnosis for Detection of Lacunar Infarcts in MR Images
Yoshikazu UCHIYAMA, Ryujiro YOKOYAMA, Takeshi HARA, Hiroshi FUJITA,
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Abstract(in English) The detection of asymptomatic lacunar infarcts in MRI images is an important task for radiologists to prevent the occurrence of serious cerebral infarctions. However, it is difficult for radiologists to identify the lacunar infarcts correctly in MRI images. Therefore, we developed a computer-aided diagnosis scheme for detection of lacunar infarcts in order to assist radiologists' interpretation as a "second opinion." We first segmented the cerebral region in the T1-weighted image by using a region growing technique. For identifying the initial candidates of lacunar infarcts, white top-hat transform and multiple-phase binarization were then applied to the T2-weighted image. 12 features, i.e., the locations, density differences from T1-and T2-weighted images, nodular components, and nodular & linear components were determined in the initial candidates regions. The rule-based schemes and an artificial neural network with 12 features were employed for distinguishing between lacunar infarcts and false positives. The sensitivity for detection of lacunar infarcts was 96.8% (90/93) with 0.69 FPs per image. Our computerized scheme might be useful in assisting radiologists for identifying lacunar infarcts in MR images.
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Keyword(in English) Lacunar infarct / Magnetic resonance imaging (MRI) / Computer-aided Diagnosis (CAD)
Paper # MI2006-94
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Conference Information
Committee MI
Conference Date 2007/1/19(1days)
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Registration To Medical Imaging (MI)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Computer-aided Diagnosis for Detection of Lacunar Infarcts in MR Images
Sub Title (in English)
Keyword(1) Lacunar infarct
Keyword(2) Magnetic resonance imaging (MRI)
Keyword(3) Computer-aided Diagnosis (CAD)
1st Author's Name Yoshikazu UCHIYAMA
1st Author's Affiliation Department of Intelligent Image Information, Graduate School of Medicine, Gifu University()
2nd Author's Name Ryujiro YOKOYAMA
2nd Author's Affiliation Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
3rd Author's Name Takeshi HARA
3rd Author's Affiliation Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
4th Author's Name Hiroshi FUJITA
4th Author's Affiliation Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
Date 2007-01-26
Paper # MI2006-94
Volume (vol) vol.106
Number (no) 509
Page pp.pp.-
#Pages 2
Date of Issue