VoxBo (www.voxbo.org) is a distributed processing framework, developed at the Center of Functional Neuroimaging at the University of Penssylvania, with the purpose of processing and analyzing brain functional MRI data. 
VoxBo is written in C++, and it is escentially a fair translation of some SPM functionality, originally written in Matlab. Along with the software, VoxBo defines two new image formats:
1) .cub, a 3D image format, used for anatomical data and single EPI frames. It is important to notice that this format defines a proper origin field, lacking, for instance, in Analyze.
2) .tes, a 4D image format, designed to contain the temporal EPI information. It is worth noticing that this format organizes the voxels in such a way to optimize longitudinal (temporal) voxel access.
Furthermore, the tes format is compressed through encoding of the functional voxels alone, i.e., background voxels are not stored for each frame, but only for the first one.

In this work we present the necessary IO classes that allow ImageFileReader and ImageFileWriter to properly identify, read and write .cub files only. We will provide .tes support in a subsequent work.
This effort is carried out with the purpose of letting VoxBo implementors to take advantage of ITK's power and overcome the limitations currently afflicting VoxBo.

Support for "cub" image format

This documents describes a set of classes to integrate digital topology in the
Insight Toolkit, with an application to homotopic thinning. The skeletonization
filter implemented using our set of classes is twice as fast as BinaryThinningImageFilter
and can be used on images of any dimension.

Digital Topology

We consider 2-D and 3-D digital binary images characterized by their well-composedness. Well-composed images exhibit important topological and geometrical properties not shared by their ill-composed counterparts. 
These properties have important implications for various algorithms used by the ITK community such as thinning algorithms and Marching Cubes. We introduce two image filters which repair images that are ill-composed such that the output images are well-composed.

Well-Composed Image Filters for Repairing 2-D and 3-D Binary Images

There is nowadays an increasing need in interaction between discrete surfaces (2-manifolds) and images. More specifically, segmentation and registration of n-dimensional images are taking advantage of a priori geometrical information, most often provided as discrete 2-manifolds.
Most of the publicly available libraries are oriented either toward mesh processing or image processing.
Through a careful study we will show that none of those libraries are complete enough to fullfill image, surface and joint image-surface interaction.
We propose to implement in ITK library a powerful 2-manifold data structure. The choice of ITK was driven by the fact that it provides the best base framework along with a strong n-dimensional image kernel. Based on a Quad-Edge data structure, it has been specifically tailored not only to represent
orientable 2-manifolds (surfaces of real objects) but also ease further processing.
We illustrate the integration of the design into ITK as a native object, enhancing existing algorithms.
We also illustrate the power of the new design for further surface processing.

ItkQuadEdgeMesh: A Discrete Orientable 2-Manifold Data Structure for Image Processing

The computation speed for distance transforms becomes important in a wide variety of image processing applications. Current ITK library filters do not see any benefit from a multithreading environment. We introduce a three-dimensional signed parallel implementation of the exact Euclidean distance transform algorithm developed by Maurer et al. with a theoretical complexity of O(n/p) for n voxels and p threads. Through this parallelization and efficient use of data structures we obtain approximately 3
times mean speedup on standard tests on a 4-processor machine compared with the current ITK exact Euclidean distance transform filter.

Parallel N-Dimensional Exact Signed Euclidean Distance Transform

Grayscale dilation and erosion are basic transformations of mathematical morphology. Used together
or with other transformations, they are very useful tools for image analysis. However, they can be very
time consuming, especially with 3D images, and with large structuring elements. Several algorithms
have been created to decrease the computation time, some of them with some limitations of shape of
structuring element. We have implemented several algorithms, studied their performance in different
conditions, and shown that all of them are more efficients than the others in certain conditions.
 We finally introduce a new structuring element class and a some meta filter designed to select the best
algorithm depending on the image and the structuring element, and to smoothely integrate the different
algorithms available in the toolkit.

Consolidated morphology

A common usage when working with Fourier transform is to shift the
the image to put the zero-frequency component in the center of the
image. This contribution comes with a filter to perform this
transform.

FFT Shift

This document describes a filter called itkOrthogonalBisectImageFilter, which bisects an input image and copies each divided region into two separate output images. Pixel values are copied while maintaining their original pixel location. The output images are of the same size, dimension, and pixel type as the input image. Pixels that have not been copied from the input image are set to a user-specified value. This filter is restricted to bisection along a cardinal axis, i.e., in a direction orthogonal to a bisecting slice (line for 2D images or plane for 3D images). Submitted with this document is the source code for the filter and source code for demonstrating filter usage via an image input as an argument. Also included is source code for testing the functionality of the filter.

Orthogonal Bisection of an Image

This document describes a filter called itkMultipleUnlabeledImagesToLabeledImageFilter, which takes multiple images as input and generates a single image of labeled pixels. Pixels to be labeled for each image are determined by an intensity threshold. Submitted with this document is the source code for the filter and source code for demonstrating filter usage via an image input as an argument. Also included is source code for testing the functionality of the filter.

itkMultipleUnlabeledImagesToLabeledImageFilter

The current ITK release (2.8) already contains a filter that enables permuting coordinate axes of an image: PermuteAxesImageFilter. PermuteAxesTransform provides similar functionality in form if ITK transform. It enables permuting axes as a part of image resampling or it may be combined with other transforms. Such techniques save system resources as they does not require an extra filter to be present in a pipeline.