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


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.

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This research has been supported by Tehran University of Medical Sciences & health Services grant 90-04-30-15836. Also, the authors would like to thank Research Center for Biomedical Technology & Robotics, RCBTR for supporting and providing an environment to carry on this project.

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Correspondence to Alireza Ahmadian.

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Serej, N.D., Ahmadian, A., Kasaei, S. et al. A robust keypoint extraction and matching algorithm based on wavelet transform and information theory for point-based registration in endoscopic sinus cavity data. SIViP 10, 983–991 (2016). https://doi.org/10.1007/s11760-015-0849-2

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  • Endoscopic sinus images
  • Repeatable and reproducible keypoints
  • NMI
  • Modified ICP
  • Gaussian weighted function