Uchiyama Y, Yokoyama R, Ando H, Asano T, Kato H, Yamakawa H, Yamakawa H, Hara T, Iwama T, Hoshi H, Fujita H. Computer-aided diagnosis scheme for detection of lacunar infarcts on MR images.
Acad Radiol 2007;
14:1554-61. [PMID:
18035284 DOI:
10.1016/j.acra.2007.09.012]
[Citation(s) in RCA: 20] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2006] [Revised: 09/07/2007] [Accepted: 09/13/2007] [Indexed: 11/19/2022]
Abstract
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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