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Structure-Based Evaluation Methodology for Curvilinear Structure Detection Algorithms

机译:基于结构的曲线结构检测算法评估方法

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Curvilinear structures are useful features, particularly in medical image analysis. Typically, a pixel-wise comparison with manually specified ground truth is used for performance evaluation. In this paper we propose a novel structure-based methodology for evaluating the performance of curvilinear structure detection algorithms. We consider the two aspects of performance, namely detection rate and detection accuracy, separately. This is in contrast to their mixed handling in earlier approaches that typically produces biased impression of detection quality. The proposed performance measures provide a more informative and precise performance characterization. A series of experiments in the context of retinal vessel detection are presented to demonstrate the advantages of our approach.
机译:曲线结构是有用的功能,特别是在医学图像分析中。通常,将具有手动指定的基本事实的逐像素比较用于性能评估。在本文中,我们提出了一种新颖的基于结构的方法来评估曲线结构检测算法的性能。我们分别考虑性能的两个方面,即检测率和检测精度。这与它们在早期方法中的混合处理相反,后者通常会产生检测质量的偏差印象。拟议的绩效指标提供了更多信息和精确的绩效表征。在视网膜血管检测的背景下进行了一系列实验,以证明我们方法的优势。

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