Multi-modal 3D Image Registration Based on Estimation of Non-rigid Deformation
This paper presents a novel approach for registration of 3D images based on optimal free-form rigid transformation. A proposal consists in semiautomatic image segmentation reconstructing 3D object surfaces in medical images. The proposed extraction technique employs gradients in sequences of 3D medical images to attract a deformable surface model by using imaging planes that correspond to multiple locations of feature points in space, instead of detecting contours on each imaging plane in isolation. Feature points are used as a reference before and after a deformation. An issue concerning this relation is difficult and deserves attention to develop a methodology to find the optimal number of points that gives the best estimates and does not sacrifice computational speed. After generating a representation for each of two 3D objects, we find the best similarity transformation that represents the object deformation between them. The proposed approach has been tested using different imaging modalities by morphing data from Histology sections to match MRI of carotid artery.
Keywords3D image matching non-rigid deformation estimation wavelet
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- 1.Mironenko, A., Song, X.B.: Image registration by minimization of residual complexity. In: IEEE Computer Soc. Conf. on Computer Vision and Pat. Recog. USA, pp. 49–56 (2009)Google Scholar
- 2.Xai, M., Liu, B.: Image Registration by Super-Curves. IEEE Transactions on Image Processing 13(5) (2004)Google Scholar
- 3.Zhu, Z., Hanson, A.R., Riseman, E.M.: Generalized Parallel-Perspective Stereo Mosaics from Airborne Video. IEEE Trans. on Pattern Analysis and Machine Intel. 26(2) (2004)Google Scholar
- 4.Adiga, U., Malladi, R., Gonzalez, R., Ortiz, C.: High-Thoughput Analysis of Multispectral Images of Breast Cancer Tissue. IEEE Transactions on Image Processing 15(8) (2006)Google Scholar
- 7.Pouderoux, J.: Global Contour Lines Reconstruction in Topographic Maps (2007)Google Scholar
- 8.Sumengen, B., Manjunath, B.S.: Graph Partitioning Active Contours (GPAC) for Image Segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(4) (2006)Google Scholar
- 9.Lazaridis, G., Petrou, M.: Image Registration Using the Walsh Transform. IEEE Transactions on Image Processing 15(8) (2006)Google Scholar
- 10.Zayer, R., Rossl, C., Karmi, Z., Seidel, H.: Harmonic Guidance for Surface Deformation Journal: Computer Graphics Forum, vol. 24(3), pp. 601–609 (2005)Google Scholar
- 11.Kempeneers, P., et al.: Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection. IEEE Trans. on Geoscience and Remote Sensing 43(3) (2005)Google Scholar
- 12.Kuman, R.: Snakes, Active Contour Models: Implements snakes or active contour models for image segmentation, Matmal (2010)Google Scholar
- 13.Alarcón-Aquino, V., Starostenko, O., et al.: Initialisation and Training Procedures for Wavelet Networks Applied to Chaotic Time Series. J. of Eng. Intelligent Systems 18(1), 1–9 (2010)Google Scholar
- 15.Borgefors, G.: Digital Transformations in Digital Images. Computer Vision, Graphics and Image Processing 34 (1986)Google Scholar