title: Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data.
abstract: Segmentation of multiple sclerosis (MS) lesion is important for many neuroimaging studies. In this paper, we propose a novel algorithm for automatic segmentation of MS lesions from multi-channel MR images (T1W, T2W and FLAIR images). The proposed method is an extension of Li et al.'s algorithm in [1], which only segments the normal tissues from T1W images. The proposed method is aimed to segment MS lesions, while normal tissues are also segmented and bias field is estimated to handle intensity inhomogeneities in the images. Another contribution of this paper is the introduction of a nonlocal means technique to achieve spatially regularized segmentation, which overcomes the influence of noise. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm.
authors.affiliation: School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China; Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA.
authors.forename: Jingjing
authors.initials: J
authors.lastname: Gao
authors.affiliation: Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: lchunming@gmail.com.
authors.forename: Chunming
authors.initials: C
authors.lastname: Li
authors.affiliation: Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China.
authors.forename: Chaolu
authors.initials: C
authors.lastname: Feng
authors.affiliation: School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
authors.forename: Mei
authors.initials: M
authors.lastname: Xie
authors.affiliation: School of Computer Science and Technology, Shandong University, Jinan, Shandong 250100, China.
authors.forename: Yilong
authors.initials: Y
authors.lastname: Yin
authors.affiliation: Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D001921:Brain / Q000473:pathology; D005189:False Positive Reactions; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Bias field estimation; Energy minimization; Lesion segmentation; Multi-channel MR images; Nonlocal means