Automatic blood vessel extraction for CTA images

XIAO Ruo-xiu; YANG Jian; SONG Ling; LIU Yue

doi:10.3788/OPE.20142202.0443

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Automatic blood vessel extraction for CTA images

Information Sciences | 更新时间：2020-08-13

- Automatic blood vessel extraction for CTA images
- Optics and Precision Engineering Vol. 22, Issue 2, Pages: 443-450(2014)
- 作者机构：
  
  北京理工大学光电学院光电成像技术与系统教育部重点实验室北京,100081
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142202.0443
  CLC： TP391.4
- Received：10 June 2013，
  
  Published：20 February 2014
- 稿件说明：
移动端阅览
肖若秀,杨健,宋凌等. 应用于医学三维影像的血管结构自动提取[J]. 光学精密工程, 2014,22(2): 443-450

XIAO Ruo-xiu,YANG Jian,SONG Ling etc. Automatic blood vessel extraction for CTA images[J]. Editorial Office of Optics and Precision Engineering, 2014,22(2): 443-450
肖若秀,杨健,宋凌等. 应用于医学三维影像的血管结构自动提取[J]. 光学精密工程, 2014,22(2): 443-450 DOI： 10.3788/OPE.20142202.0443.

XIAO Ruo-xiu,YANG Jian,SONG Ling etc. Automatic blood vessel extraction for CTA images[J]. Editorial Office of Optics and Precision Engineering, 2014,22(2): 443-450 DOI： 10.3788/OPE.20142202.0443.

摘要

针对从计算机断层扫描血管造影术(CTA)的三维体数据中提取血管结构信息通常需要大量的人为介入操作的问题

提出了一种全自动血管分割方法。首先

采用多尺度增强滤波器对数据中的管状结构进行增强

剔除非管状结构

并过滤噪声。然后

利用Sigmoid函数作用于梯度图像产生水平集的速度图像

并通过测地活动轮廓模型迭代逼近真实的三维血管轮廓。最后

通过拉普拉斯算法对提取的血管表面网格进行平滑处理

获得光滑的血管曲面。实验采用胸部和颈部的CTA体数据对算法进行测试

结果表明:该方法无需人工干预即可从CTA体数据中准确地提取出血管的三维信息;血管中心线提取的平均误差为0.26 mm

直径测量平均误差为0.16 mm

分割精度满足临床血管疾病辅助诊疗的要求。

Abstract

When vascular structure information is segmented from three-dimensional data of Computed Tomography Angiography(CTA)

it usually involves considerable amount of human intervention. To improve the vessel extraction efficiency

a fully automatic extraction method was proposed to segment blood vessels from three-dimensional data sets. Firstly

a multi-scale enhancement filter was developed to enhance the tubular-like structures

by which the non-tubular structures and noise were effectively removed. Then

a gradient image was combined with Sigmoid function to produce the speed image

and the Geodesic Active Contour(GAC) level set was utilized to approximate the real three dimensional vascular outline. Thereafter

the obtained vasculatures were processed by Laplacian smoothing function and a smoothed vascular surface was obtained. The proposed method was validated on both chest and neck CTA data. Experimental results show that blood vessels can be segmented accurately and automatically without human intervention. According to the phantom experiments

the average errors estimated for centerline and diameter of extracted vessels are 0.26 mm and 0.16 mm respectively. As there is no human interaction involved in the segmentation

the developed method can be utilized for the computer-assisted diagnosis of vascular related diseases in clinical practices.

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references

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