Kernel-based transforms such as the thin plate spline are frequently used to model deformations in medical
imaging. The existing implementation in ITK is capable of being used to warp images, but does not
work in the registration framework. The existing implementation is inefficient, requiring recomputation
of all cached values at every parameter change, and the Jacobian calculation is not implemented. By
reversing the roles of the fixed and moving parameters, the transform can be adapted for registration use.
We present modified classes which are more efficient, and calculate the Jacobian correctly.

Improvements to the itk::KernelTransform and Subclasses

Often times developers face the need to connect ITK and VTK pipelines to handle
image data. For this purpose, image data importer and exporter classes 
have been implemented both in ITK and VTK. However, as evident from frequent questions in 
the ITK and VTK users and developers mailing list, usage of these classes is not 
straight forward. Furthermore, the difficulty gets more challenging with the need to 
handle different pixel types. For these reasons, an effort was made to write Image IO classes that 
encapsulate all the intricacies and provide users with easy to use API. This article 
describes these Image IO classes and present an example to demonstrate how to use
these classes.

Seamless VTK-ITK pipeline connection for image data handling

This workshop features peer-reviewed submissions that describe the available data and software of MICCAI.

Open Source and Open Data for MICCAI - Announcement and Program

The subject of the paper is to introduce a new class of 4D filters to itk.While it is very easy to load a 3D volume stored as a series of DICOM images with ITK, some convenience functions that are already available for 3D volumes are not yet available for the 4D case. Especially for MR data (and to a certain limit also for CT data) it is very important to look at the full 4D series, since the radiologist usually does not make a diagnosis based on a single volume but on the full time series of volumes which shows the temporal flow of the contrast agent. As an example, for breast imaging the radiologist typically examines parameter maps like MIPt (maximum intensity over time), TTP (time to peak) or WI (Wash-In). These parameter maps are the 3D result of post-processing the entire 4D time series. The goal of proposing the denoted 4D filters is to provide ITK with functionality that enables a postprocessing job to handle the entire 4D series as one entity and not as a loose collection of 3D volumes.

How to use the itk::NaryElevateImageFilter and itk::UnaryRetractImageFilter for the post-processing of medical 4D data

Combining some labeled images is a quite common and useful task in image analysis.
That's something we may want to do to prepare the markers for a watershed from
markers for example, or to write the output of several segmentations in a single
file.
This article comes with three new filters to make the combination
of labeled images easier with ITK.
This contribution also try to provide an implementation better integrated with the
ITK pipeline model than the itkMultipleUnlabeledImagesToLabeledImageFilter contribution.

Combining labeled images with ITK

Although Gabor filtering is quite prevalent in the computer vision community for such tasks as 
texture segmentation and motion analysis, such capabilities are conspicuously absent from the 
Insight Toolkit. The contribution described in this paper attempts to remedy this deficiency by 
introducing two new classes: itkGaborKernelFunction and itkGaborImageSource.

Go-Go Gabor Gadgetry

ManagedITK generates wrappers around ITK for .NET languages. These wrappers can be used with any CLR language, including C#, VB.NET, IronPython, and others. ManagedITK is useful for a number of reasons, including the ability to rapidly create graphical user interfaces using Windows Forms (also known as System.Windows.Forms). Full source code and many in-depth examples accompany this article. Pre-compiled .NET assemblies can also be downloaded for easy integration into standalone C# applications.

ManagedITK: .NET Wrappers for ITK

This article presents the itk::ComposeRGBAImageFilter.

ComposeRGBA

This article provides an implementation of our non-parametric diffeomorphic image registration algorithm generalizing Thirion's demons algorithm. Within the Insight Toolkit (ITK), the demons algorithm is implemented as part of the finite difference solver framework. We show that this framework can be extended to handle diffeomorphic transformations. The source code is composed of a set of reusable ITK filters and classes. In addition to an overview of our implementation, we provide a small example program that allows the user to compare the different variants of the demons algorithm.

Diffeomorphic Demons Using ITK's Finite Difference Solver Hierarchy

This paper describes the Insight Toolkit (ITK) Spherical Wavelet object: itkSWaveletSource. This
ITK object is an implementation of a paper by Schr¨oder and W. Sweldens, “Spherical Wavelets: Effi-
ciently Representing Functions on the Sphere” [8], with pseudo-code given in their paper entitled “Spherical
wavelets: Texture processing” [7]. In these papers, Swelden et. al. show how to do decompose a
scalar signal defined on a spherical mesh into spherical wavelet coefficients (analysis step, also called
forward transform), and vice-versa (synthesis step, also called inverse transform). We have implemented
the spherical wavelet transform in ITK entitled itkSWaveletSource object, which will take the scalar
function defined on a spherical mesh as input and apply spherical wavelet analysis and synthesis on it.
In this paper, we describe our code and provide the user with enough details to reproduce the results
which we present in this paper. This filter has a variety of applications including shape representation
and shape analysis of brain surfaces, which was the initial motivation for this work.
This paper is accompanied with the source code, input data, parameters and output data that the
authors used for validating the spherical wavelet transform described in this paper.