Faster scaling algorithms for network problems
SIAM Journal on Computing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
An efficient cost scaling algorithm for the assignment problem
Mathematical Programming: Series A and B
Residuals + directional gaps = skeletons
Pattern Recognition Letters
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation and Analysis of Monocular Building Extraction From Aerial Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Retinal Blood Vessel Segmentation by Means of Scale-Space Analysis and Region Growing
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Supervised Evaluation Methodology for Curvilinear Structure Detection Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Fast Skeletonization of Spatially Encoded Objects
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Separation of the retinal vascular graph in arteries and veins based upon structural knowledge
Image and Vision Computing
Structure-based evaluation methodology for curvilinear structure detection algorithms
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Segmentation of thin structures in volumetric medical images
IEEE Transactions on Image Processing
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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.