Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images
Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images
复制标题
DOI：
10.1109/42.918469
复制
发表时间：
2001-03-01
影响因子：
10.6
通讯作者：
Rothfus, WE
中科院分区：
文献类型：
--
作者：
Liu, YX;Collins, RT;Rothfus, WE
本文主要研究从正常和病理的三维神经图像中提取理想正中矢状面（iMSP），主要挑战是病理大脑中存在的结构不对称性，以及临床实践中常见的各向异性、不均匀采样的图像数据。我们提出了一种基于边缘的互相关方法，该方法将平面拟合问题分解为在每个切片上发现二维对称轴，然后对平面参数进行鲁棒估计。定量评价了该算法对大脑不对称、输入图像偏移和图像噪声的容忍度。我们发现该算法可以从输入的具有1)大的不对称病灶的三维图像中提取出iMSP；2)任意初始旋转偏移量；3)低信噪比或高偏置场。将iMSP算法与基于互信息配准最大化的方法进行了比较，发现在不利条件下iMSP算法表现出更好的性能。最后，通过iMSP算法计算的正中矢状面与两位训练有素的神经放射学家估计的正中矢状面之间没有统计学上的显著差异。
This paper focuses on extracting the ideal midsagittal plane (iMSP) from three-dimensional (3-D) normal and pathological neuroimages, The main challenges in this work are the structural asymmetry that mag exist in pathological brains, and the anisotropic, unevenly sampled image data that is common in clinical practice. We present an edge-based, cross-correlation approach that decomposes the plane fitting problem into discovery of two-dimensional symmetry axes on each slice, followed by a robust estimation of plane parameters. The algorithm's tolerance to brain asymmetries, input image offsets and image noise is quantitatively evaluated. We find that the algorithm can extract the iMSP from input 3-D images with 1) large asymmetrical lesions; 2) arbitrary initial rotation offsets; 3) low signal-to-noise ratio or high bias field. The iMSP algorithm is compared with an approach based on maximization of mutual information registration, and is found to exhibit superior performance under adverse conditions. Finally, no statistically significant difference is found between the midsagittal plane computed by the iMSP algorithm and that estimated by two trained neuroradiologists.