SurgIPC: a convex image perspective correction method to boost surgical keypoint matching

Keypoint detection and matching is a fundamental step in surgical image analysis. However, existing methods are not perspective invariant and thus degrade with increasing surgical camera motion amplitude. One approach to address this problem is by warping the image before keypoint detection. However, existing warping methods are inapplicable to surgical images, as they make unrealistic assumptions such as scene planarity.

We propose Surgical Image Perspective Correction (SurgIPC), a convex method, specifically a linear least-squares (LLS) one, overcoming the above limitations. Using a depth map, SurgIPC warps the image to deal with the perspective effect. The warp exploits the theory of conformal flattening: it attempts to preserve the angles measured on the depth map and after warping, while mitigating the effects of image resampling.

We evaluate SurgIPC under controlled conditions using a liver phantom with ground-truth camera poses and with real surgical images. The results demonstrate a significant improvement in the number of correct correspondences when SurgIPC is applied. Furthermore, experiments on downstream tasks, including keyframe matching and 3D reconstruction using structure-from-motion (SfM), highlight significant performance gains.

SurgIPC improves keypoint matching. The use of LLS ensures efficient and reliable computations. SurgIPC can thus be easily included in existing computer-aided surgery systems.