Study of Keypoints Detectors and Descriptors Performance on X-Ray Images Compared to the Visible Light Spectrum Images

In this work, we study the performance of wide-used keypoints detection and description algorithms: Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Oriented FAST and Rotated BRIEF (ORB), Binary Robust Invariant Scalable Keypoints(BRISK), Accelerated KAZE(AKAZE), which were originally developed for images taken in visible light but widely applied in the fields where images are taken in a different spectrum. We compare the quality of algorithms and their robustness to various image transformations. The algorithms’ performance is tested on two image sets in the different spectra: digital X-Ray images and images taken in the visible spectrum. Each dataset captures complex scenes with many objects and partial occlusions. Geometrical transformations (rotation, shearing, scaling), linear color transformations, Gaussian blur are applied to the images. Then the detection and description algorithms are tested on the original and transformed images. The repeatability and number of corresponding points are calculated to assess detection algorithms. The ratio of correctly matched descriptors together with the ratio of the distances between the query descriptor, the nearest descriptor, and the second matched descriptor is computed to evaluate descriptors’ quality. The algorithms showed different behavior on different spectra. SURF demonstrated to be the best X-ray keypoint detector and for the visible spectrum, it shares first place with AKAZE detector. SIFT is the best descriptor in both spectra. The strong and weak points of each algorithm are discussed in the paper.