A Fast and Accurate Feature-Matching Algorithm for Minimally-Invasive Endoscopic Images

The ability to find image similarities between two distinct endoscopic views is known as feature matching, and is essential in many robotic-assisted minimally-invasive surgery (MIS) applications. Differently from feature-tracking methods, feature matching does not make any restrictive assumption about the chronological order between the two images or about the organ motion, but first obtains a set of appearance-based image matches, and subsequently removes possible outliers based on geometric constraints. As a consequence, feature-matching algorithms can be used to recover the position of any image feature after unexpected camera events, such as complete occlusions, sudden endoscopic-camera retraction, or strong illumination changes. We introduce the hierarchical multi-affine (HMA) algorithm, which improves over existing feature-matching methods because of the larger number of image correspondences, the increased speed, and the higher accuracy and robustness. We tested HMA over a large (and annotated) dataset with more than 100 MIS image pairs obtained from real interventions, and containing many of the aforementioned sudden events. In all of these cases, HMA outperforms the existing state-of-the-art methods in terms of speed, accuracy, and robustness. In addition, HMA and the image database are made freely available on the internet.