Outline. Medical Image Processing with Orientation Scores. Mathematica in the BioMIM lab. The Biomedical Image Analysis group

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Medical Image Processing with Orientation Scores Erik Franken*, Remco Duits, Markus van Almsick,

nl About our Research Group Eindhoven University of Technology Department of Biomedical

Biomedical Image Analysis group Mathematica in the BioMIM lab Starring: Petr Šereda (Pilsen,

campus license for Mathematica The need for bigmath kernel servers Bigmath1: Tyan TX46, 4x

2Ghz, 64GB + 3 older servers Use of ParallelMathematica Our Mathematica Infrastructure Full

1 Medical Image Processing with Orientation Scores Erik Franken*, Remco Duits, Markus van Almsick, Bart ter Haar Romeny * About our Research Group Eindhoven University of Technology Department of Biomedical Engineering Mathematica Technology Conference 2006 October 13th 2006, Champaign 1 2 The Biomedical Image Analysis group Mathematica in the BioMIM lab Starring: Petr Šereda (Pilsen, Czech Republic) Tim Peeters (Echt, The Netherlands) 3 4 Our Mathematica Infrastructure Full campus license for Mathematica The need for bigmath kernel servers Bigmath1: Tyan TX46, 4x Opteron 2.2Ghz, 32GB Bigmath2: Tyan VX50, 4x Dualcore Opteron 2.2Ghz, 64GB + 3 older servers Use of ParallelMathematica Our Mathematica Infrastructure Full campus license for Mathematica The need for bigmath kernel servers Bigmath1: Tyan TX46, 4x Opteron 2.2Ghz, 32GB Bigmath2: Tyan VX50, 4x Dualcore Opteron 2.2Ghz, 64GB + 3 older servers Use of ParallelMathematica 5 6 1

MathVisionTools Mathematica in Bachelor Education

derivatives Geometry driven diffusion Orientation score

algorithms in Mathematica Competitive element 6 10 6

net 4 3 2 11 7 8 Example: Differential invariants 1 st

(T-junctions) For example Rotation invariant T-junction

The retina contains receptive fields of varying sizes

Primary visual cortex is multi-orientation Measurement

image f(x,y) to orientation score U f (x,y,θ) with

visual cortex are orientation-specific Strong

2 MathVisionTools Mathematica in Bachelor Education Computer Vision Library for Mathematica: Gaussian derivatives Geometry driven diffusion Orientation score functions Image transformations DICOM import/export Image Analysis for Pathology. Groups of 8 2nd year students Invent image analysis algorithms in Mathematica Competitive element weeks project Example: Differential invariants 1 st order (edges) 2 nd order (ridges) 3 rd order (T-junctions) For example Rotation invariant T-junction detection: 9 10 Biological Inspiration 1. The retina contains receptive fields of varying sizes multi-scale sampling device 2. Primary visual cortex is multi-orientation Measurement in Primary Visual Cortex Orientation Scores From 2D image f(x,y) to orientation score U f (x,y,θ) with position (x,y) and orientation θ Cells in the primary visual cortex are orientation-specific Strong connectivity between cells that respond to (nearly) the same orientation An orientation score is a function on the Euclidean motion group Bosking et al., J. Neuroscience 17: ,

3 Our approach: Image Processing via Orientation Scores Initial image Invertible Orientation Score Transformation Design considerations: reconstruction, directional, spatial localization, quadrature Orientation score transformation Enhancement operation Inverse orientation score transformation Enhanced image Segment structures of interest Segmented structures The Diffusion Equation on Images f = image u = scale space of image D = diffusion tensor Linear diffusion Perona&Malik Coherence-enhancing diff. 15 t = 0 16 The Diffusion Equation on Images f = image u = scale space of image D = diffusion tensor Linear diffusion Perona&Malik Coherence-enhancing diff. Left-invariant derivatives s Diffusion in orientation Diffusion tangent to oriented structures θ θ curvature Evolving orientation score Diffusion orthogonal to oriented structures ξ, η, and θ are left-invariant derivatives on Euclidean motion group, i.e. Rotating tangent space coordinate basis L g i U = i L gu, i {ξ,η,θ} x ξ η t = y θ η ξ 3

4 Example diffusion kernels How to Choose Conductivity Coefficients Oriented regions: D 11 and D33 small, D22 large and κ according to estimate θ Non-oriented regions: D 11 large, θ D22=D33 large, κ = 0 ξ θ η ξ x η y 19 t=0 Results t = 10 Results Size: 128 x 128 x 64 Size: 128 x 128 x Collagen image Collagen image Size: 200 x 200 x 64 t=0 20 Size: 200 x 200 x 64 t =

Other PDE: the stochastic completion field Resolvent

line continuation based on random walker prior 25 26

function on the translation group An orientation score

convolution (on translation group) Filling Gaps in

5 Other PDE: the stochastic completion field Resolvent of linear PDE It renders probability density field for line continuation based on random walker prior Convolution on Orientation Scores An image is a function on the translation group An orientation score is a function on the Euclidean motion group Normal convolution (on translation group) Filling Gaps in Curves G-convolution, where G is the Euclidean motion group 27 (From M.Sc. thesis by Renske de Boer) 28 Enhancing edges in Medical images Source image Local information Result

6 Using Mathematica Mathematica is helpful in solving the math (e.g. non-commuting operators) NDSolve in mathematica is not usable for our type of PDEs as far as I know PDE solver is written in C++, linked with Mathlink Typical problems of our PDE Highly anisotropic, not aligned with grid Non-commuting operators Convection + diffusion Acknowledgements Remco Duits Markus van Almsick Bart ter Haar Romeny Bart Janssen Arjen Ricksen Renske de Boer For questions / more references about this work, contact e.m.franken@tue.nl

Invertible Orientation Scores of 3D Images

Invertible Orientation Scores of 3D Images Michiel Janssen Supervisors/Collaborators: Remco Duits Guido Janssen Gonzalo Sanguinetti Jorg Portegies Marcel Breeuwer Javier Olivan Bescos Project: ERC Lie