A robust keypoint extraction and matching algorithm based on wavelet transform and information theory for point-based registration in endoscopic sinus cavity data

Abstract

Feature extraction is one of the most important steps in processing endoscopic data. The extracted features should be invariant to image scale and rotation to provide a robust matching across a substantial range of affine distortions and changes in 3D space. In this study, a method is proposed on the basis of the dual-tree complex wavelet transform. First, a map is estimated for each scale, and then a Gaussian weighted additive function (GWAF) is determined. Keypoints are selected from local peaks of GWAF. The matching and registration are performed by applying normalized mutual information and our modified iterative closest point. Results are reported in terms of robustness to rotation, noise, color, brightness, number of keypoints, index of matching and execution time for the building, standard clinical and phantom sinus datasets. Although the results are comparable to that of the speeded up robust features, scale invariant feature transform, and the Harris method, they are more robust to the variations in rotation, brightness, color, and noise than those obtained from other methods. Registration errors obtained for consequent frames for building, clinical and phantom datasets are 0.97, 1.46 and 1.1 mm, respectively.