Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features. (April 2017) - Xiao, Ruoxiu; Ding, Hui; Zhai, Fangwen; Zhao, Tong; Zhou, Wenjing; Wang, Guangzhi
Highlights: A novel method is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset. It combines vascular grayscale and shape features based on Dempster–Shafer evidence theory. A cost function is modeled to differentially punish the mis-segmentation of the background and blood vessels. The segmentation ratio coefficient is proposed to prevent over-segmentation. Abstract: Background and objective: In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset. Methods: First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved. Second, enhanced vesselness image is obtained according to the shape-based multiscale vascular enhancement filter. Third, a new reconstructed vascular image is established according to the fusion of vascular grayscale and shape features using Dempster–Shafer evidence theory; subsequently, the corresponding segmentation structures are obtained. Finally, according to the noise distribution characteristic of the data, segmentation ratio coefficient, which increased linearly from top to bottom, is proposed to control the segmentation result, thereby preventing over-segmentation. Results: Experiment results show that, through the proposed method, vascular structures can be detected not only when both grayscale and shape features are strong, but also when either of them is strong. Compared with traditional grayscale feature- and shape feature-based methods, it is better in the evaluation of testing in segmentation accuracy, and over-segmentation and under-segmentation ratios. Conclusions: The proposed grayscale and shape features combined vascular segmentation is not only effective but also accurate. It may be used for diagnosis of vascular diseases and planning of neurosurgery.