Structural performance evaluation of curvilinear structure detection algorithms with application to retinal vessel segmentation

Curvilinear structures are useful features in a variety of applications, particularly in medical image analysis. Compared to other commonly used features such as edges and regions, there is relatively few work on performance evaluation methodologies for curvilinear structure detection algorithms. For instance, a pixel-wise comparison with ground truth has been used in all recent publications on vessel detection in retinal images. In this paper we propose a novel structure-based methodology for evaluating the performance of 2D and 3D curvilinear structure detection algorithms. We consider the two aspects of performance, namely detection rate and detection accuracy, separately, in contrast to their mixed handling in earlier approaches that typically produces biased impression of detection quality. By doing so, the proposed performance measures give us a more informative and precise performance characterization. Experiments on both synthetic and real examples will be given to demonstrate the advantages of our approach.

► We discuss the weaknesses of existing non-structural performance evaluation methods.
► We present a structural performance evaluation method.
► Experiments are presented to demonstrate the advantages of our approach.