Paper Abstract and Keywords |
Presentation |
2007-09-20 15:10
Development of automated method for detection of multiple sclerosis candidate regions based on brain magnetic resonance images Daisuke Yamamoto, Hidetaka Arimura (Kyushu Univ.), Shingo Kakeda (Univ. of Occupational and Environmental Health), Yasuo Yamashita (Kyushu Univ. Hospital), Seiji Kumazawa, Fukai Toyofuku, Yoshiharu Higashida (Kyushu Univ.), Yukunori Korogi (Univ. of Occupational and Environmental Health) MI2007-45 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The severity and symptom of multiple sclerosis (MS) depend on its location, shape, and area. It is very important to evaluate the temporal change of MS regions in terms of location, shape, and area for estimation of MS progression. Our aim of this study was to develop an automated method for detection of MS candidate regions based on three types of brain magnetic resonance (MR) images, i.e., T1-, T2-weighted images, and fluid attenuated inversion-recovery (FLAIR) images. The MS candidate regions were identified based on a multiple gray level thresholding technique and a region growing technique on a subtraction image between a T1-image and a FLAIR image. The candidate regions were determined by monitoring the interval changes of image feature values for region growing based on pixel value. Eight image features were determined for each candidate region, and many false positive regions were removed by using simple rules and a support vector machine (SVM). We applied our method to 24 slices of four MS cases, which included 80 MS regions. As a result, 87.5 % of MS regions were detected without false positives per slice. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
computer-aided diagnosis (CAD) / multiple sclerosis (MS) / magnetic resonance imaging (MRI) / image feature analysis / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 107, no. 220, MI2007-45, pp. 51-52, Sept. 2007. |
Paper # |
MI2007-45 |
Date of Issue |
2007-09-13 (MI) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MI2007-45 |
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