基于多通道MRI数据的多发性硬化病变非局部正则化分割
Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data.
作者信息
Gao Jingjing, Li Chunming, Feng Chaolu, Xie Mei, Yin Yilong, Davatzikos Christos
机构信息
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.
Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
出版信息
Magn Reson Imaging. 2014 Oct;32(8):1058-66. doi: 10.1016/j.mri.2014.03.006. Epub 2014 Apr 24.
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.
多发性硬化症（MS）病灶的分割对于许多神经影像学研究而言至关重要。在本文中，我们提出了一种用于从多通道磁共振图像（T1加权、T2加权和液体衰减反转恢复序列图像）中自动分割MS病灶的新算法。所提出的方法是对文献[1]中Li等人算法的扩展，该算法仅从T1加权图像中分割正常组织。所提出的方法旨在分割MS病灶，同时也对正常组织进行分割，并估计偏置场以处理图像中的强度不均匀性。本文的另一个贡献是引入了一种非局部均值技术来实现空间正则化分割，从而克服了噪声的影响。实验结果证明了所提算法的有效性和优势。
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