Zhao Fengjun, Liang Jimin, Chen Dongmei, Wang Chuan, Yang Xiang, Chen Xueli, Cao Feng
Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
Department of Cardiology, Chinese People's Liberation Army General Hospital, Beijing 100853, China.
Med Phys. 2015 Jul;42(7):4043-54. doi: 10.1118/1.4922200.
The goal of this paper is to address three problems existing in vessel extraction of murine hindlimb. First, the bone can hardly be separated from blood vessels because the intensity of contrast enhanced blood vessels is similar to that of bones. Second, as an automatic blood vessel segmentation method, the vesselness method is sensitive to sharp boundaries, resulting in false positive effect in nonvascular regions. Finally, thin blood vessels are always broken after segmentation because of the low signal-to-noise ratio.
The proposed automatic segmentation method for bone and blood vessel in this paper includes three important modules. (1) To eliminate the interference of bones on the segmentation of blood vessels, the authors employ split Bregman method to segment bones in the first place. (2) The authors propose an edge extension strategy to cope with the false positive effect of the vesselness method on the sharp boundaries of hindlimb after the removal of bones. Then, the authors segment the blood vessels using the vesselness method combined with multiscale bi-Gaussian filtering. (3) The authors reconnect the broken blood vessels after segmentation based on centerline and morphological dilation.
The bones' segmentation from the murine hindlimbs was conducted using the split Bregman, manual, and thresholding methods, respectively. Compared with the thresholding method, the split Bregman method could finely segment the bones from blood vessels, and the results were comparable to that of manual segmentation. After removing bones, the vesselness method combined with the bi-Gaussian filtering with and without edge extension was performed. The vesselness results with the edge extension strategy could effectively eliminate the false positive effect on sharp boundaries in nonvascular regions. Some of the blood vessels segmented by thresholding from the vesselness results were disconnected. Thus, the authors employed the vascular connection method based on centerline and morphological dilation to connect the broken blood vessels. Compared with the vascular connection utilizing the spatial-variant and -invariant morphological closing methods, the proposed vascular connection method reconnected the broken blood vessels and meanwhile maintained the nonbroken ones unchanged.
Our proposed method is suitable for the segmentation of bones and blood vessels in murine hindlimbs. For the segmentation of bones, the split Bregman method improves the distinguishability between bones and blood vessels, since both the intensity information and the geometrical size are exploited. For the segmentation of blood vessels, vesselness method with the edge extension strategy eliminates the false positive effect on the nonvascular sharp boundaries. After segmentation, the proposed vascular connection method based on centerline and morphological dilation can reconnect the broken blood vessels without affecting the nonbroken ones.
本文旨在解决小鼠后肢血管提取中存在的三个问题。第一,由于增强对比度后的血管强度与骨骼相似,骨骼很难与血管分离。第二,作为一种自动血管分割方法,血管性方法对尖锐边界敏感,在非血管区域会产生假阳性效应。最后,由于信噪比低,细血管在分割后总是会断裂。
本文提出的骨骼和血管自动分割方法包括三个重要模块。(1)为了消除骨骼对血管分割的干扰,作者首先采用分裂Bregman方法分割骨骼。(2)作者提出了一种边缘扩展策略,以应对去除骨骼后血管性方法对后肢尖锐边界产生的假阳性效应。然后,作者结合多尺度双高斯滤波,使用血管性方法分割血管。(3)作者在分割后基于中心线和形态学膨胀重新连接断裂的血管。
分别使用分裂Bregman方法、手动方法和阈值法对小鼠后肢的骨骼进行分割。与阈值法相比,分裂Bregman方法能够将骨骼与血管精细地分割开,结果与手动分割相当。去除骨骼后,对采用和未采用边缘扩展的双高斯滤波结合血管性方法进行了实验。采用边缘扩展策略的血管性结果能够有效消除非血管区域尖锐边界上的假阳性效应。通过阈值法从血管性结果中分割出的一些血管是断开的。因此,作者采用基于中心线和形态学膨胀的血管连接方法来连接断裂的血管。与利用空间可变和不变形态学闭运算方法进行的血管连接相比,本文提出的血管连接方法重新连接了断裂的血管,同时保持未断裂的血管不变。
我们提出的方法适用于小鼠后肢骨骼和血管的分割。对于骨骼分割,分裂Bregman方法利用强度信息和几何尺寸,提高了骨骼与血管之间的可区分性。对于血管分割,采用边缘扩展策略的血管性方法消除了非血管尖锐边界上的假阳性效应。分割后,本文提出的基于中心线和形态学膨胀的血管连接方法能够重新连接断裂的血管,而不影响未断裂的血管。