基于三维冠状动脉CTA图像的半自动血管分割方法

Semi-automatic vessel segmentation of 3D medical images applied to coronary vessel segmentation

目的从患者胸腔部位的计算机断层血管造影(computed tomography angiongraphy,CTA)图像中分离出冠状动脉血管,并实现三维的可视化,以便医生对由冠状动脉病变引起的心血管疾病进行诊断和治疗。方法在传统区域生长的基础上提出一种半自动的血管分割方法。首先采用基于灰度值的主动脉预生长,然后进行基于自适应阈值的冠状动脉生长,最后进行后期处理,得到最终分割结果。结果通过这种半自动的区域生长得到的结果,在三维上进行可视化后能够较清晰地判断出血管的粗细、形状、狭窄等情况,且得到的模型也可用于后期的相关计算。结论基于传统区域生长算法的半自动血管分割方法提高了冠状动脉分割的适应性和有效性,能更好地对冠状动脉CTA图像进分割提取。

Objective To segment coronary arteries from coronary computed tomography angiography（ CTA） images of patients and realize the three-dimensional visualization,so that doctors can observe patients＇ coronary arteries in three-dimension and are able to appeal diagnosis and treatment to cardiovascular diseases caused by coronary artery lesion. Methods This paper proposed a semi-automatic method of vessel segmentation based on traditional region growth algorithm. Firstly,Preliminary growth of the aorta based on gray value was proposed. Then the coronary arteries were segmented by regional growth based on self-adaption threshold.Finally,we did certain post processing. Results The segmentation results demonstrated that three-dimensional visualization modeling results in this paper could provide clear information about vessels ＇size,shape,and whether stenosis and cut. Also,the results could be used for further related computation. Conclusions This semi-automatic vessel segmentation based on traditional region grow algorithm its adaption and effectiveness and makes a better segmentation of coronary arteries.