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

机译:冠状动脉血管造影中的中心线跟踪改进的种子点检测算法

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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:本文介绍了一种新方法,用于检测冠状动脉血管造影中血管中心线的自动追踪初始种子点。众所周知,血管跟踪算法在几种特征提取方法中是快速有效的。然而,由于种子点检测和计算效率的完整性之间不适当的权衡,他们中的大多数患者遭受了不完整的结果。强加严格的验证规则会减少背景迹线的数量,但导致更虚假的否定和更多的计算时间。我们表明,使用在船舶边界点计算的梯度矢量的几何特性作为验证标准,提高了种子点检测算法的性能。结果说明了所提出的方法改善了性能和计算时间的先前方法。

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