title: Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy.
abstract: White matter lesions (WMLs) are commonly observed on the magnetic resonance (MR) images of normal elderly in association with vascular risk factors, such as hypertension or stroke. An accurate WML detection provides significant information for disease tracking, therapy evaluation, and normal aging research. In this article, we present an unsupervised WML segmentation method that uses Gaussian mixture model to describe the intensity distribution of the normal brain tissues and detects the WMLs as outliers to the normal brain tissue model based on extreme value theory. The detection of WMLs is performed by comparing the probability distribution function of a one-sided normal distribution and a Gumbel distribution, which is a specific extreme value distribution. The performance of the automatic segmentation is validated on synthetic and clinical MR images with regard to different imaging sequences and lesion loads. Results indicate that the segmentation method has a favorable accuracy competitive with other state-of-the-art WML segmentation methods.
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Wang
authors.affiliation: Institute of Diagnostic and Interventional Radiology, Sixth Affiliated People's Hospital, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Xiaoer
authors.initials: X
authors.lastname: Wei
authors.affiliation: Institute of Diagnostic and Interventional Radiology, Sixth Affiliated People's Hospital, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Chun
authors.initials: C
authors.lastname: Hui
authors.affiliation: CREATICS; CNRS UMR 5220; Inserm 1044; INSA Lyon; Villeurbanne, France.
authors.forename: Yuemin
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China. Electronic address: suzhang@sjtu.edu.cn.
authors.forename: Su
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D000465:Algorithms; D000925:Anticoagulants / Q000737:chemistry; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016013:Likelihood Functions; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D008875:Middle Aged; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D011336:Probability; D015203:Reproducibility of Results; D012307:Risk Factors; D020521:Stroke / Q000473:pathology; D066127:White Matter / Q000473:pathology*
chemical_list: D000925:Anticoagulants
keywords: Extreme value theory; Gaussian mixture model; Outlier detection; White matter lesions