Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data

Jingjing Gao; Chunming Li; Chaolu Feng; Mei Xie; Yilong Yin; Christos Davatzikos

doi:10.1016/j.mri.2014.03.006

Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data

Magn Reson Imaging. 2014 Oct;32(8):1058-66. doi: 10.1016/j.mri.2014.03.006. Epub 2014 Apr 24.

Authors

Jingjing Gao¹, Chunming Li², Chaolu Feng³, Mei Xie⁴, Yilong Yin⁵, Christos Davatzikos⁶

Affiliations

¹ 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.
² 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.
³ Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China.
⁴ School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
⁵ School of Computer Science and Technology, Shandong University, Jinan, Shandong 250100, China.
⁶ Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.

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.

Keywords: Bias field estimation; Energy minimization; Lesion segmentation; Multi-channel MR images; Nonlocal means.

MeSH terms

Algorithms
Artificial Intelligence
Automation
Brain / pathology
False Positive Reactions
Fuzzy Logic
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Models, Statistical
Multiple Sclerosis / pathology*
Pattern Recognition, Automated / methods
Reproducibility of Results
Sensitivity and Specificity

Grants and funding

R01 EB009234/EB/NIBIB NIH HHS/United States