Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine.
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 from 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. These results indicate the proposed method would be useful for assisting neuroradiologists in assessing the MS in clinical practice.
Yamamoto
Daisuke
D
Kyushu University, Fukuoka, Japan.
Arimura
Hidetaka
H
Kakeda
Shingo
S
Magome
Taiki
T
Yamashita
Yasuo
Y
Toyofuku
Fukai
F
Ohki
Masafumi
M
Higashida
Yoshiharu
Y
Korogi
Yukunori
Y
D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity