SimpleITK Fundamental Concepts

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Presentation transcript:

SimpleITK Fundamental Concepts 1Hans J. Johnson, 2Bradley C. Lowekamp, 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: 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 SimpleITK2Numpy: Numpy2SimpleITK: sitk.GetArrayFromImage – Data copy in numpy array (mutable). sitk.GetArrayViewFromImage – Data view in numpy array (immutable). Numpy2SimpleITK: new_image = sitk.GetImageFromArray – Data copy in 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.