Automatic centerline detection of small three-dimensional vessel structures

Yuanzhi Cheng,Xin Hu,Shinichi Tamura,Jinke Wang,Yadong Wang

doi:10.1117/1.jei.23.1.013007

Yuanzhi Cheng, Xin Hu + Show 3 more

Open Access

https://doi.org/10.1117/1.jei.23.1.013007

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Abstract

Vessel centerline detection is very important in many medical applications. In the noise and low-contrast regions, most existing methods may only produce an incomplete and disconnected extraction of the vessel centerline if no user guidance is provided. A robust and automatic method is described for extraction of the vessel centerline. First, we perform small vessel enhancement by processing with a set of line detection filters, corresponding to the 13 orientations; for each voxel, the highest filter response is kept and added to the image. Second, we extract vessel centerline segment candidates by a thinning algorithm. Finally, a global optimization algorithm is employed for grouping and selecting vessel centerline segments. We validate the proposed method quantitatively on a number of synthetic data sets, the liver artery and lung vessel. Comparisons are made with two state-of-the-art vessel centerline extraction methods and manual extraction. The experiments show that our method is more accurate and robust that these state-of-the-art methods and is, therefore, more suited for automatic vessel centerline extraction.

Highlights

Vessel centerline has been used in computing edge gradients and searching for border positions,[1] to derive video-densitometric profiles,[2] for measurement of vessel diameter,[3] for calculation of lesion symmetry,[4] and as the basis for threedimensional (3-D) reconstruction of vessel segments or of the entire arterial tree.[3,5]Many techniques in the literature propose to detect vessel centerline by thinning algorithm.[6,7,8] These algorithms are notoriously sensitive to noise and can result in ambiguous results at bifurcations
A comparison is made with two state-of-the-art centerline detection methods using the liver artery from abdominal computed tomography (CT)
For methods which have been designed to work on segmented tubular structures, such as ours, quantitative validation on medical images is a challenge because a ground truth centerline is typically not defined

Summary

Introduction

Vessel centerline has been used in computing edge gradients and searching for border positions,[1] to derive video-densitometric profiles,[2] for measurement of vessel diameter,[3] for calculation of lesion symmetry,[4] and as the basis for threedimensional (3-D) reconstruction of vessel segments or of the entire arterial tree.[3,5]Many techniques in the literature propose to detect vessel centerline by thinning algorithm.[6,7,8] These algorithms are notoriously sensitive to noise and can result in ambiguous results at bifurcations. The axial tangent of the seed is generated This tangent helps to define a cross-sectional plane (normal plane) by shifting forward the current one along the tangential direction, on which the vessel is segmented and the axial point is calculated as the centroid of the planar segmentation. This process keeps iterating until a termination criterion is met or the user preempts the tracing

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