Gong Zhaoxuan, Zhao Dazhe, Li Chunming, Tan Wenjun, Davatzikos Christos
Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China.
Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA.
Adv Vis Comput. 2015 Dec;9474:521-530. doi: 10.1007/978-3-319-27857-5_47. Epub 2015 Dec 18.
The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic robust algorithm for lesion segmentation based on MR images is proposed. This method takes full advantage of the decomposition of MR images into the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity. An energy function is defined in term of the property of true image and bias field. The energy minimization is proposed for seeking the optimal segmentation result of lesions and white matter. Then postprocessing operations is used to select the most plausible lesions in the obtained hyperintense signals. The experimental results show that our approach is effective and robust for the lesion segmentation.
多发性硬化症病变的检测对于许多神经影像学研究而言至关重要。本文提出了一种基于磁共振（MR）图像的新型自动鲁棒病变分割算法。该方法充分利用了将MR图像分解为表征组织物理特性的真实图像和解释强度不均匀性的偏置场这一特性。根据真实图像和偏置场的特性定义了一个能量函数。提出通过能量最小化来寻求病变和白质的最优分割结果。然后使用后处理操作在所获得的高强度信号中选择最合理的病变。实验结果表明，我们的方法对于病变分割是有效且鲁棒的。
