Evaluation of Interest Point Detectors for Non-planar, Transparent Scenes

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.

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