SimpleITK Fundamental Concepts

Slides:

Advertisements

Similar presentations

3D Transformations Assist. Prof. Dr. Ahmet Sayar

Advertisements

Computer Graphics: 3D Transformations

Finite Element Method CHAPTER 4: FEM FOR TRUSSES

ITK Deformable Registration

Steady-state heat conduction on triangulated planar domain May, 2002

Image Registration  Mapping of Evolution. Registration Goals Assume the correspondences are known Find such f() and g() such that the images are best.

CS 691 Computational Photography Instructor: Gianfranco Doretto Image Warping.

Medical Image Registration Kumar Rajamani. Registration Spatial transform that maps points from one image to corresponding points in another image.

Interpolation By Radial Basis Function ( RBF ) By: Reihane Khajepiri, Narges Gorji Supervisor: Dr.Rabiei 1.

Render Cache John Tran CS851 - Interactive Ray Tracing February 5, 2003.

Image Registration: Demons Algorithm JOJO

1 Geometrical Transformation 2 Outline General Transform 3D Objects Quaternion & 3D Track Ball.

Jinxiang Chai CSCE441: Computer Graphics Coordinate & Composite Transformations 0.

Image Warping : Computational Photography Alexei Efros, CMU, Fall 2006 Some slides from Steve Seitz

2/7/2001Hofstra University – CSC290B1 Review: Math (Ch 4)

Non-Rigid Registration. Why Non-Rigid Registration  In many applications a rigid transformation is sufficient. (Brain)  Other applications: Intra-subject:

CSci 6971: Image Registration Lecture 26: BSpline Transforms April 20, 2004 Prof. Chuck Stewart, RPI Dr. Luis Ibanez, Kitware Prof. Chuck Stewart, RPI.

Lapped Textures SIGGRAPH 2000 Emil Praun Adam Finkelstein Hugues Hoppe.

Image Warping : Computational Photography Alexei Efros, CMU, Fall 2008 Some slides from Steve Seitz

Image Warping : Computational Photography Alexei Efros, CMU, Fall 2005 Some slides from Steve Seitz

Ch. 2: Rigid Body Motions and Homogeneous Transforms

Image warping/morphing Digital Video Special Effects Fall /10/17 with slides by Y.Y. Chuang,Richard Szeliski, Steve Seitz and Alexei Efros.

Image Warping Computational Photography Derek Hoiem, University of Illinois 09/27/11 Many slides from Alyosha Efros + Steve Seitz Photo by Sean Carroll.

National Alliance for Medical Image Computing Registration in Slicer3 Julien Jomier Kitware Inc.

What Does the Scene Look Like From a Scene Point? Donald Tanguay August 7, 2002 M. Irani, T. Hassner, and P. Anandan ECCV 2002.

Transformation of Graphics

A COMPARISON OF SOME SHALLOW WATER TEST CASES IN HOMME USING NUMERICALLY AND ANALYTICALLY COMPUTED TRANSFORMATIONS Numerical Approximations of Coordinate.

CS 551/651 Advanced Computer Graphics Warping and Morphing Spring 2002.

Chapter 10: Graphics MATLAB for Scientist and Engineers Using Symbolic Toolbox.

Digital Image Processing CCS331 Image Interpolation 1.

CSci 6971: Image Registration Lecture 3: Images and Transformations March 1, 2005 Prof. Charlene Tsai.

Raster Concepts.

Image Registration as an Optimization Problem. Overlaying two or more images of the same scene Image Registration.

Jinxiang Chai CSCE441: Computer Graphics 3D Transformations 0.

Jinxiang Chai Composite Transformations and Forward Kinematics 0.

1 Complex Images k’k’ k”k” k0k0 -k0-k0 branch cut   k 0 pole C1C1 C0C0 from the Sommerfeld identity, the complex exponentials must be a function.

NA-MIC National Alliance for Medical Image Computing Registering Image Volumes in Slicer Steve Pieper.

Aquarius Level-3 Binning and Mapping Fred Patt. Definitions Projection - any process which transforms a spatially organized data set from one coordinate.

Advanced Multimedia Warping & Morphing Tamara Berg.

Two-Dimensional Geometric Transformations A two dimensional transformation is any operation on a point in space (x, y) that maps that point's coordinates.

1 Graphics CSCI 343, Fall 2015 Lecture 10 Coordinate Transformations.

Geometric Transformations

Multimedia Programming 07: Image Warping Keyframe Animation Departments of Digital Contents Sang Il Park.

January 19, y X Z Translations Objects are usually defined relative to their own coordinate system. We can translate points in space to new positions.

Affine Geometry.

Data Visualization Fall The Data as a Quantity Quantities can be classified in two categories: Intrinsically continuous (scientific visualization,

ITKv4 – Refactoring Status – June Level Sets What is in Alpha – 08 – Refactored Fast-Marching What must be done by Beta (Sept 15) – Remove ITKv3.

III- 1 III 3D Transformation Homogeneous Coordinates The three dimensional point (x, y, z) is represented by the homogeneous coordinate (x, y, z, 1) In.

Principal Warps: Thin-Plate Splines and the Decomposition of Deformations 김진욱 ( 이동통신망연구실 ; 박천현 (3D 모델링 및 처리연구실 ;

Jinxiang Chai CSCE441: Computer Graphics Coordinate & Composite Transformations 0.

Image Warping 2D Geometric Transformations

Jo˜ao Carreira, Abhishek Kar, Shubham Tulsiani and Jitendra Malik University of California, Berkeley CVPR2015 Virtual View Networks for Object Reconstruction.

Image warping/morphing Digital Visual Effects, Spring 2006 Yung-Yu Chuang 2005/3/15 with slides by Richard Szeliski, Steve Seitz and Alexei Efros.

Computer Graphics CC416 Week 15 3D Graphics.

SimpleITK Historical Overview: Standing on the Shoulders of Giants

SimpleITK Historical Overview: Standing on the Shoulders of Giants

Notebook Development and Testing

Notebook Development and Testing

SimpleITK Setup and Schedule

SimpleITK Setup and Schedule

SimpleITK Fundamental Concepts

CSCE441: Computer Graphics Coordinate & Composite Transformations

Theorems about LINEAR MAPPINGS.

Geometric Objects and Transformations (II)

CSCE441: Computer Graphics 2D/3D Transformations

SimpleITK Fundamental Concepts

The Image The pixels in the image The mask The resulting image 255 X

SimpleITK Setup and Schedule

Warm Up 6.3 Complete the chart for the given counterclockwise rotations about the origin. 90o 180o 270o R(3, -3) S(1, 4) Use.

Image Registration  Mapping of Evolution

Presentation transcript:

SimpleITK Fundamental Concepts 1Hans J. Johnson, 2,4Bradley C. Lowekamp, 2,3Ziv Yaniv 1The University of Iowa 2National Institutes of Health 3TAJ Technologies Inc. 4MSC LLC

Transforms All global transformation are of the form*: *Except translation.

Transforms Free-Form Deformation: You set: sparse grid of control points with uniform spacing, B0..3 cubic B-spline basis functions. You set: Spline order (default is cubic) Number of grid points per axis (mesh size) Spatial domain manually: origin; physical dimension; direction cosine matrix image based: BSplineTransformInitializerFilter Transformation is identity outside the user defined domain.

Transforms Displacement Field: Dense set of vectors representing the displacement in a given spatial domain. You set: Spatial domain and deformation: manually: origin; physical dimension; direction cosine matrix; vector values. image based: vector image which is emptied of its contents. Transformation is identity outside the user defined domain.

Transforms Composite transformation: Represents multiple transformations applied one after the other. T0(T1(T2(…Tn(p)...))) Stack based semantics – first in last applied. composite_transform = sitk.Transform(T0) composite_transform.AddTransform(T1) When used as the optimized transformation in registration (SetInitialTransform), only the parameters of the last transformation, Tn, are optimized.

Images An image is defined by: Pixel type + spatial dimensionality. Physical region in space occupied by the image as specified by: origin, spacing, size, and direction cosine matrix.

Images SimpleITK2Python, SimpleITK2R: Python2SimpleITK, R2SimpleITK: sitk.GetArrayFromImage/as.array – Data copied into numpy/R array (mutable). sitk.GetArrayViewFromImage – Data view in numpy array (immutable). Python2SimpleITK, R2SimpleITK: sitk.GetImageFromArray/as.image – Data copied into SimpleITK image. Set all of the parameters defining the physical region in space: new_image.CopyInformation or new_image.SetOrigin, new_image.SetSpacing, new_image.SetDirection

Resample: Image + Transform Resampling, three elements (assuming arbitrary interpolation method): Image – the image we resample in coordinate system m. transformation – T(fp) = mp maps points from coordinate system f to m. resampling grid – uniform set of points which will be mapped by the transformation.

Resample: Image + Transform Specifying the resampling grid Use an existing image. Use origin, size, spacing, and direction cosine. Unexpected results: errors in resampling grid specification or transformation.

Registration – Coordinate Systems Three coordinate systems: Fixed, Virtual, Moving. Three transformations: Tf(vp) = fp Tm(vp) = mp Topt(mp) = mp’ Most often Tf=I, the fixed and virtual coordinate systems coincide.

Registration - Framework Optimizers: Exhaustive Nelder-Mead Simplex/Amoeba Powell 1+1 evolutionary GradientDescent GradientDescentLineSearch RegularStepGradientDescent ConjugateGradientLineSearch L-BFGS-B Similarity metrics: MeanSquares Demons Correlation ANTSNeighborhoodCorrelation JointHistogramMutualInformation MattesMutualInformation Multi-resolution framework. Masks. Sampling strategies.