Comparative Study of Feature Localization Methods for Endoscopy Image Matching

The purpose of this work is to determine which is the best general method of feature localization for image matching in endoscopy images. To accomplish this, we conduct an exhaustive analysis of ten well-known feature detectors, descriptors, and learned algorithms, such as SIFT, FAST, SURF, ORB, BRIEF, BRISK, FREAK, HARRIS, DFM, and LoFTR. The analysis is performed across six challenging medical datasets, including cardiorespiratory endoscopy, human laparoscopy, bronchoscopy, gastroscopy, rabbit laparoscopy, and pig laparoscopy. This framework is highly diverse, containing a variety of textures, camera motions, tissue deformations, and visual barriers. To determine which technique is the best on average, we perform a qualitative analysis of the inliers and a quantitative analysis using the number of keypoints, number of matches, number of inliers, computational cost, sparsity of the inliers, recall and 1-precision. To complete the study, we considered the sequential and template modes, as they are highly used in computer vision. Furthermore, we examine how those features may be exploited in the reconstruction of 3D shapes from visual cues.