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An Improved Seed Point Detection Algorithm for Centerline Tracing in Coronary Angiograms

机译:改进的种子点检测算法在冠状动脉造影中线追踪中的应用

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This paper presents a new method to detect initial seed points for automatic tracing of the vessel center lines in coronary angiograms. Vessel tracing algorithms are known to be fast and efficient among several feature extraction methods. However, most of them suffer from incomplete results due to inappropriate trade-off between the completeness of seed point detection and computational efficiency. Imposing strict validation rules decreases the number of background traces, but results in more false negatives and more computation time. We show that using the geometrical properties of gradient vectors calculated at vessel boundary points as a validation criterion, improves the performance of the seed point detection algorithm. The results illustrate that the proposed method improves upon the prior method in both performance and computation time.
机译:本文提出了一种新的方法来检测初始种子点,以自动追踪冠状动脉造影中的血管中心线。众所周知,在几种特征提取方法中,船只跟踪算法是快速有效的。但是,由于种子点检测的完整性和计算效率之间的不适当折衷,它们中的大多数都遭受了不完整的结果的困扰。强加严格的验证规则会减少背景迹线的数量,但会导致更多的假阴性和更多的计算时间。我们表明,使用在容器边界点计算的梯度向量的几何特性作为验证标准,可以提高种子点检测算法的性能。结果表明,所提方法在性能和计算时间上均优于现有方法。

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