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2D and 3D Fourier Based Discrete Radon Transform Amir Averbuch With Ronald Coifman – Yale University Dave Donoho – Stanford University Moshe Israeli –

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Presentation on theme: "2D and 3D Fourier Based Discrete Radon Transform Amir Averbuch With Ronald Coifman – Yale University Dave Donoho – Stanford University Moshe Israeli –"— Presentation transcript:

1 2D and 3D Fourier Based Discrete Radon Transform Amir Averbuch With Ronald Coifman – Yale University Dave Donoho – Stanford University Moshe Israeli – Technion, Israel Yoel Shkolnisky – Yale University

2 Research Activities Polar processing (Radon, MRI, diffraction tomography, polar processing, image processing) Dimensionality reduction (hyperspectral processing, segmentation and sub-pixel segmentation, remote sensing, performance monitoring, data mining) Wavelet and frames (error correction, compression) Scientific computation (prolate spheroidal wave functions) XML (fast Xpath, handheld devices, compression) Nano technology (modeling nano batteries, controlled drug release, material science simulations) - interdisciplinary research with material science, medicine, biochemistry, life sciences

3 Participants: previous and current Dr Yosi Keller – Gibbs Professor, Yale Yoel Shkolnisky - Gibbs Professor, Yale Tamir Cohen – submitted his Ph.D Shachar Harrusi – Ph.D student Ilya Sedelnikov - Ph.D student Neta Rabin - Ph.D student Alon Shekler - Ph.D student Yossi Zlotnick - Ph.D student Nezer Zaidenberg - Ph.D student Zur Izhakian – Ph.D student

4 Computerized tomography

5 CT-Basics


7 7 Typical CT Images

8 8 CT Scanner

9 CT-Basics


11 Introduction – CT Scanning X-Ray from source to detector Each ray reflects the total absorption along the ray Produce projection from many angles Reconstruct original image

12 2D Continuous Radon Transform The 2D continuous Radon is defined as

13 2D Continuous Fourier Slice Theorem 2D Fourier slice theorem where is the 2D continuous Fourier transform of f. The1D Fourier transform with respect to s of is equal to a central slice, at angle θ, of the 2D Fourier transform of the function f(x,y).

14 Discretization Guidelines We will look for both 2D and 3D definitions of the discrete Radon transform with the following properties: Algebraic exactness Geometric fidelity Rapid computation algorithm Invertibility Parallels with continuum theory

15 2D Discrete Radon Transform - Definition Summation along straight lines with θ<45° Trigonometric interpolation at non- grid points

16 2D Discrete Radon Transform – Formal Definition For a linewe define where andis the Dirichlet kernel with

17 2D Definition - Illustration

18 2D Discrete Radon Definition – Cont. For a line we define where and

19 2D Definition - Illustration

20 Selection of the Parameter t Radon({y=sx+t},I) Sum over all lines with non trivial projections. Same arguments for basically vertical lines.

21 Selection of the Parameter m Periodic interpolation kernel. Points out of the grid are interpolated as points inside the grid. Summation over broken line. Wraparound effect.

22 Selection of the Parameter m – Cont. Pad the image prior to using trigonometric interpolation. Equivalent to elongating the kernel. No wraparound over true samples of I. Summation over true geometric lines. Required:

23 The Translation Operator translates the vectorusing trigonometric interpolation. Example: translation of a vector with The translation operator

24 The Shearing Operator For the slope θ of a basically vertical line: For the slope θ of a basically horizontal line: Motivation: The shearing operator translates the samples along an inclined line into samples along horizontal/vertical line.

25 The Shearing Operator - Illustration


27 Alternative Definition of the Discrete Radon Transform and are padded versions of along the y-axis and the x-axis respectively

28 2D Discrete Fourier Slice Theorem Using the alternative discrete Radon definition we prove: where

29 The discrete Radon transform was defined for a continuous set of angles. For the discrete set Θ the discrete Radon transform is discrete in both Θ and t. For the set Θ, the Radon transform is rapidly computable and invertible. Discretization of θ

30 Illustration of Θ Θ2Θ2 Θ1Θ1

31 Fourier Slice Theorem Revisited For where We define the pseudo-polar Fourier transform:

32 The Pseudo-Polar Grid The pseudo-polar grid is defined by The pseudo polar Fourier transform is the sampling of on a special pointset called the pseudo-polar grid.

33 The Pseudo-Polar Grid - Illustration P2P2 P1P1


35 The Fractional Fourier Transform Can be computed for anyusingoperations. The fractional Fourier transform is defined as We can use the fractional Fourier transform to compute samples of the Fourier transform at any spacing.

36 Resampling in the Frequency Domain Given samples in the frequency domain, we define the resampling operator

37 Given we can compute using operations by using 1D Fourier transform. 2D Discrete Radon Algorithm We show an for computing. G k,n

38 2D Discrete Radon Algorithm – Cont. Description: (PP 1 I) 1. Pad both ends of the y-direction of the image I and compute the 2D DFT of the padded image. The results are placed in I’. 2. Resample each row k in I’ using the operator G k,n with α = 2k/n. 3. Flip each row around its center.

39 Papers Optical Snow Analysis using the 3D-Xray Transform, submitted. Fast and Accurate Polar Fourier Transform, submitted. Discrete diffraction tomography, submitted. 2D Fourier Based Discrete Radon Transform, submitted. Algebraically accurate 3-D rigid registration, IEEE Trans. on Signal Proessing. Algebraically Accurate Volume Registration using Euler's Theorem and the 3-D Pseudo-Polar FFT, submitted. Fast Slant Stack: A notion of Radon Transform for Data in a Cartesian Grid which is Rapidly Computible, Algebraically Exact, Geometrically Faithful and Invertible, SIAM Scientific Computing. Pseudo-polar based estimation of large translations, rotations and scalings in images, IEEE Trans. on Image Processing. The Angular Difference Function and its application to Image Registration, IEEE PAMI. 3D Discrete X-Ray Transform, Applied and Computational Harmonic Analysis 3D Fourier Based Discrete Radon Transform, Applied and Computational Harmonic Analysis Digital Implementation of Ridgelet Packets, Beyond wavelets – chapter in book. Multidimensional discrete Radon transform, chapter in book. The pseudopolar FFT and its Applications, Research Report A signal processing approach to symmetry detection, IEEE Trans. on Image Processing Fast and accurate pseudo-polar protein docking, submitted.

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