Abstract
This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg–Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter adjusting and the trust region method, the error between 3D reconstructed vessel projection and the actual vessel can be minimized. Then, CBOCD (curvature and brightness order curve descriptor) is proposed to indicate the degree of the self-occlusion of blood vessels during angiography. Next, the error matrix constructed from the error of epipolar matching is used in point pairs matching of the vascular through dynamic programming. Finally, the recorded radius of vessels helps to construct ellipse cross-sections and samples on it to get a point set around the centerline and the point set is converted to mesh for reconstructing the surface of vessels. The validity and applicability of the proposed methods have been verified through experiments that result in the significant improvement of 3D reconstruction accuracy in terms of average back-projection errors. Simultaneously, due to precise point-pair matching, the smoothness of the reconstructed 3D coronary artery is guaranteed.
Highlights
Academic Editors: Simone PalazzoA large number of humans die from coronary artery disease every year
The 3D spatial structure of the blood vessels superimposed on a 2D image may overlap each other in the contrast image and obstruct the doctor’s observation, which is the main disadvantage of X-ray imaging
In order to evaluate the effect of our proposed algorithm on improving the accuracy of the 3D reconstruction, the directly matching method relying on the order of points on blood vessels is recorded as Match 1, the dynamic programming algorithm with fixed step size is recorded as Match 2, and the dynamic programming algorithm with self-adaption step size according to CBOCD proposed in this paper is recorded as Match 3
Summary
A large number of humans die from coronary artery disease every year. An effective way to check for coronary artery disease is coronary angiography. The 3D spatial structure of the blood vessels superimposed on a 2D image may overlap each other in the contrast image and obstruct the doctor’s observation, which is the main disadvantage of X-ray imaging. The 3D reconstruction of the coronary arteries is achieved by using two planar angiographic images of the same blood vessel segment from different angles. The beating of the heart, the error of the two-dimensional image extraction, and the error of the system parameters affect the accuracy of the 3D reconstruction. By optimizing the spatial relationship between the planar angiography, photos can improve the reconstruction accuracy, and the spatial relationship is characterized by rotation and translation transformation
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