文件名称:1-s2.0-S016502701100522X-main_2
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The purpose of this study was to develop a computerized method for detection of multiple sclerosis
(MS) lesions in brain magnetic resonance (MR) images. We have proposed a new false positive reduction
scheme, which consisted of a rule-based method, a level set method, and a support vector machine. We
applied the proposed method to 49 slices selected 6 studies of three MS cases including 168 MS
lesions. As a result, the sensitivity for detection of MS lesions was 81.5 with 2.9 false positives per slice
based on a leave-one-candidate-out test, and the similarity index between MS regions determined by
the proposed method and neuroradiologists was 0.768 on average-The purpose of this study was to develop a computerized method for detection of multiple sclerosis
(MS) lesions in brain magnetic resonance (MR) images. We have proposed a new false positive reduction
scheme, which consisted of a rule-based method, a level set method, and a support vector machine. We
applied the proposed method to 49 slices selected 6 studies of three MS cases including 168 MS
lesions. As a result, the sensitivity for detection of MS lesions was 81.5 with 2.9 false positives per slice
based on a leave-one-candidate-out test, and the similarity index between MS regions determined by
the proposed method and neuroradiologists was 0.768 on average
(MS) lesions in brain magnetic resonance (MR) images. We have proposed a new false positive reduction
scheme, which consisted of a rule-based method, a level set method, and a support vector machine. We
applied the proposed method to 49 slices selected 6 studies of three MS cases including 168 MS
lesions. As a result, the sensitivity for detection of MS lesions was 81.5 with 2.9 false positives per slice
based on a leave-one-candidate-out test, and the similarity index between MS regions determined by
the proposed method and neuroradiologists was 0.768 on average-The purpose of this study was to develop a computerized method for detection of multiple sclerosis
(MS) lesions in brain magnetic resonance (MR) images. We have proposed a new false positive reduction
scheme, which consisted of a rule-based method, a level set method, and a support vector machine. We
applied the proposed method to 49 slices selected 6 studies of three MS cases including 168 MS
lesions. As a result, the sensitivity for detection of MS lesions was 81.5 with 2.9 false positives per slice
based on a leave-one-candidate-out test, and the similarity index between MS regions determined by
the proposed method and neuroradiologists was 0.768 on average
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