Spherical Wavelets Code Release

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* Version 1.2.2 available*  (last update: 09/03/09)


Description

In Euclidean space, the orthogonal/bi-orthogonal wavelet transform provides a tradeoff between the local nature of pixel-wise and global Fourier representation. The wavelet transform projects an image onto basis functions with compact support at different spatial locations and scales. Unfortunately, each level of the multi-scale orthogonal/bi-orthogonal wavelet transform suffers from sampling aliasing. The result is that a translation of an image by a single pixel results in dramatic changes in the wavelet coefficients. Overcomplete wavelet transforms overcome these problems by ensuring sufficient sampling at each multiresolution level.  

The overcomplete spherical wavelet transform [1, 2] extends the overcomplete wavelet transform from Euclidean to spherical images. An example of a spherical image is the representation of 2D closed surfaces as spherical images. This is done by spherically parametrizing 2D closed surfaces, so that each point on the sphere has some features describing the geometry of the original surface. One popular set of features might be the curvature of the original surface. Another set is the (x, y, z) coordinates of the original surface mesh. The application of overcomplete spherical wavelets to cortical surfaces is found in [3, 4].

We provide 5 example surfaces distributed with our code (under example_surfaces folder). They are part of the publicly available oasis dataset. Of course, this work did not arise from a vaccum. Many important previous references can be found in [1, 2, 3, 4].


Publications

The theories behind the overcomplete wavelet code we provide is based on the following papers. We note that the theories in these papers are far more general than the overcomplete wavelet codes we provide here. The papers discuss the construction of spherical steerable pyramid, which are overcomplete wavelet with oriented filters unlike the axisymmetric laplacian filters we use here. 

[1] B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. IEEE Transactions on Image Processing, 17(3):283--300, 2008. [pdf]

[2] B.T.T. Yeo, W. Ou, P. Golland. Invertible Filter Banks on the 2-Sphere. Proceedings of the International Conference on Image Processing (ICIP), 2161--2164, 2006 [pdf]

The application of the overcomplete spherical wavelets to cortical surfaces is based on the following papers.

[3] B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008 [pdf]

[4] P. Yu, B.T.T. Yeo, P.E. Grant, B. Fischl, P. Golland. Cortical Folding Development Study based on Over-complete Spherical Wavelets. Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), International Conference on Computer Vision, 2007. [pdf]


Prerequisites


Matlab Code (Latest Release)

Instructions: 


Release Comments for version 1.2.2: 


Matlab Code (Previous Release)


Acknowledgments

Support for this research is provided in part by the NAMIC (NIH NIBIB NAMIC U54-EB005149), the NAC (NIH CRR NAC P41-RR13218), the mBIRN (NIH NCRR mBIRN U24-RR021382), the NIH NINDS R01-NS051826 grant, the NSF CAREER 0642971 grant, NCRR (P41-RR14075, R01 RR16594-01A1), the NIBIB (R01 EB001550, R01EB006758), the NINDS (R01 NS052585-01) and the MIND Institute. Additional support was provided by The Autism & Dyslexia Project funded by the Ellison Medical Foundation. B.T. Thomas Yeo is funded by the A*STAR, Singapore.