The detection of stable, distinctive and rich feature point sets has been an active area of research in the field of video and image analysis. Transparency imaging, such as X-ray, has also benefited from this research. However, an evaluation of the performance of various available detectors for this type of images is lacking. The differences with natural imaging stem not only from the transparency, but -in the case of medical X-ray- also from the non-planarity of the scenes, a factor that complicates the evaluation. In this paper, a method is proposed to perform this evaluation on non-planar, calibrated X-ray images. Repeatability and accuracy of nine interest point detectors is demonstrated on phantom and clinical images. The evaluation has shown that the Laplacian-of-Gaussian and Harris-Laplace detectors show overall the best performance for the datasets used.
|Title of host publication||Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems(ACIVS 2009) 28 September - 2 October 2009, Bordeaux|
|Editors||J. Blanc-Talon, W. Philips, D. Popescu, P. Scheunders|
|Place of Publication||Berlin|
|Publication status||Published - 2009|
|Name||Lecture Notes in Computer Science|