This document describes the iteration direction optimized Image Iterators . Comparing with the original corresponding iterators, the new iterators provide automatic iteration direction optimization and give more flexible. They are more efficient when the fastest moving direction has small size. The derived template class ImagePieceWiseLinearIteratorWithIndex is a generic class that can be replace the ImageLinearIterator and ImageSliceIterator. The paper present the implementation and examples of these classes. This paper is accompanied with the source code and experiments.

optimized image iterators

Various metrics have been proposed in the literature for performing intrinsic automatic image to image registration. Among these measures, mutual information is a very popular one because of its robustness and accuracy for a wide variety of applications. In this paper, we propose a filter for performing non-rigid registration by estimating a dense deformation field derived from the mutual information metric. This filter takes place in the ITK PDE deformable registration design like the Demons algorithm of Thirion. We also show how the concept of metric flow is conceptually linked to the concept of metric derivative for a prior transformation model by the transformation jacobian. We also suggest a sparse implementation of the GetJacobian() method for reducing the computation time of a metric derivative for local transformations models.

Incorporating Metric Flows and Sparse Jacobian Transformations in ITK

ITK is a huge image analysis library, which contains lots of state of the arts
algorithms implementations. However, using it in C++ can be difficult and is
definitively bad suited for prototyping. WrapITK aims to allow classes from ITK
(and custom, classes that interact with ITK) to be "wrapped" for use with
languages like Python, Tcl, and Java.

WrapITK: Enhanced languages support for the Insight Toolkit

This document describes contributions on the
NormalizedCorrelationImageToImageMetric and the MeanSquaresImageToImageMetric of the Insight Toolkit ITK \url{www.itk.org}. For the first metric a two time speed-up can be
achieved by rewriting the code to loop only once over the fixed image. This is instead of the two times that is used in the current ITK code. The reduction in computation time comes at the cost of an additional storage of a parameters array. For both metric we have
implemented the option to use only a random subset of the fixed image voxels for calculating the metric value and its derivatives. This reduces the computation time (substantially), while convergence
properties are maintained. This paper is accompanied with the source code.

Contributions to the Normalized Correlation and the Mean Squares Metric

MINC2.0 IO support REVISED!
This revision has direction cosines and various other issues addressed.

MINC2.0 IO Support for ITK

This document describes the implementation of an algorithm that computes a generalization of the distance transform with the squared euclidean metric.

The generalization allows for interesting image operators, e.g. a morphologic dilation with euclidean ball structure elements that can vary in size across the image. Voronoi maps and the standard distance transform can be computed as well.

The algorithm is provided as an image processing filter for ITK. Several example programs demonstrate its applications.

A Generalized Squared Euclidean Distance Transform with Voronoi Maps

The current ITK release 2.6 does not contain a way to combine transforms in a structural way. This document describes a contribution to the Insight Toolkit ITK \url{www.itk.org}, which implements what we call an \doxygen{CombinationTransform}. Two ways to combine transforms are implemented, by addition and by composition. Depending on this selection, different implementations of the \code{TransformPoint()} are provided. Also the expressions for the Jacobian and the inverse transform depend on the way transforms are combined. By using function pointers expensive \code{if}-statements are avoided. This paper is accompanied with the
source code.

Combining Transforms in ITK

This report discusses various definitions and implementations of the watershed transform as well as providing an introduction to some well known techniques for applying the watershed transform to practical problems. The discussion will be focused on new and existing ITK classes.

The watershed transform in ITK - discussion and new developments

Classes to provide IO capabilities for the ITK Neural Networks library.

ITK Neural Networks IO Classes

This work describes the implementation of computational geometry algorithms developed within the Insight Toolkit (ITK): Distance Transform (DT), Voronoi diagrams, k Nearest Neighbor (kNN) transform, and finally a K Nearest Neighbor classifier for multichannel data, that is used for supervised segmentation. We have tested this algorithm for 2D and 3D medical datasets, and the results are excellent in terms of accuracy and performance. One of the strongest points of the algorithms described here is that they can be used for many other applications, because they are based on the ordered propagation paradigm. This idea consists in actually not raster scan the image but rather in start from the image objects and propagate them until the image is totally filled. This has been demonstrated to be a good approach in many algorithms as for example, computation of Distance Transforms, Voronoi Diagrams, Fast Marching, skeletons computation, etc. We show here that these algorithms have low computational complexity and it provides excellent results for clinical applications as the segmentation of brain MRI.