Richard Beare has recently introduced a new filter to efficiently labelize the connected components with ITK, and has also proposed to use the run-length encoding used in that filter to implement some of the most useful binary mathematical morphology operators: the opening by attribute. Following that idea, and after have searched a way to use the ITK's spatial objects for this task, a new set of classes have been developed to represent and manipulate the label images and the objects within them in ITK.
Those new classes have been used to implement several label images manipulation based on object attributes, as well as the binary and label specialization of some mathematical morphology filters based on the morphological reconstruction. This contribution comes with 65 new classes, and should greatly enhance the binary mathematical
morphology in ITK.

All the source codes are provided, as well as a full set of tests and several usage examples of the new classes.


Label object representation and manipulation with ITK

We present an open-source ITK implementation of a direct Fourier method for tomographic reconstruction, applicable to parallel-beam x-ray images. Direct Fourier reconstruction makes use of the central-slice theorem to build a polar 2D Fourier space from the 1D transformed projections of the scanned object, that is resampled into a Cartesian grid. Inverse 2D Fourier transform eventually yields the reconstructed image. Additionally, we provide a complex wrapper to the BSplineInterpolateImageFunction to overcome ITK’s current lack for image interpolators dealing with complex data types. A sample application is presented and extensively illustrated on the Shepp-Logan head phantom. We show that appropriate input zeropadding and 2D-DFT oversampling rates together with radial cubic b-spline interpolation improve 2D-DFT interpolation quality and are efficient remedies to reduce reconstruction artifacts.

Direct Fourier Tomographic Reconstruction Image-To-Image Filter

This document describes an addition to the Image IO Library of the Insight Toolkit (ITK). ITK has
been able to read in Analyze image files but not the Analyze Object Maps that correspond to the images.
Analyze Object maps define regions of interest that are helpful in designating locations, giving a name to
those locations, as well as display characteristics. Also, Analyze Object Maps are useful to tell someone
where to look in an image. With the lack of not being able to read/write Analyze Object Maps, a new set
of classes have been developed to read/write Analyze Object Maps and manipulate the object maps.

Reader/Writer for Analyze Object Maps for ITK

Computation of local image derivatives is an important operation in many image processing tasks that
involve feature detection and extraction, such as edges, corners or more complicated features. However,
derivative computation in discrete images is an ill-posed problem and derivative operators without
any prior smoothing are known to enhance noise. Here we present a new convolution operator, the
GaussianDerivativeOperator, that allows to calculate locally Gaussian derivatives of N order. Furthermore,
we present some useful classes and examples that make use of this new operator.

Generalized Computation of Gaussian Derivatives Using ITK

The University of Iowa’s Psychiatric Iowa Neuroimaging Consortium (PINC) has developed a program for mutual information registration of 3D brain imaging data using ITK classes, called BRAINSFit. We have written a helper class, itk::MultiModal3DMutualRegistrationHelper to simplify implementation and testing of different transform representations and optimizers. We have added a transform meeting the ITK standard, itk::ScaleVersor3DTransform. BRAINSFit is based on the registration examples from ITK, but adds new features, including the ability to employ different transform representations and optimization functions. 
Our goal was to determine best practices for registering 3D rigid multimodal MRI of the human brain. A version of the current program is employed here at PINC daily for automated processing of acquired brain images.

This project is managed on the NITRC site. http://www.nitrc.org/projects/multimodereg/

On the NITRC website, you can acquire the latest source code via SVN, or by downloading a compressed file which is generated every night. Binary versions are also available.


BRAINSFit: Mutual Information Registrations of Whole-Brain 3D Images, Using the Insight Toolkit

The original design of the ITK registration framework was based around the itk::Image class, which
assumed that the pixel axes were aligned with the coordinate system axes. The itk::OrientedImage
was added later as a subclass, but problems remain with its gradient calculations. Furthermore, general
code that uses the itk::OrientedImage will suffer an unnecessary penalty when the image is oriented
parallel to the image axes. We propose a new itk::FastOrientedImage class that alleviates these
performance problems, and a change to the design of itk::ImageToImageMetric that resolves the
gradient issue, and adds a number of additional capacities to the image metrics

Improving The Oriented Image Class For Use In The Registration Framework

In this paper we attempt to segment the lateral ventricles using the ITK provided Confidence 
Connected algorithm. An ITK application was used to explore the various input parameters 
to produce an image of acceptable presentation. Input MRI data of healthy individual 
was provided through the MIDAS repository resulting in a segmented image.

Confidence Connected Segmentation of the Lateral Ventricles

This technical report describes how to use Insight Toolkit to perform elementary ventricle segmentation on data from the Designed Database of MR Brain Images of Healthy Volunteers hosted at MIDAS.

Reproducible Ventricle Segmentation Using Insight Toolkit

This document describes an algorithm implemented using the Insight Toolkit ITK www.itk.org. This paper is 
accompanied with the source code, input data, parameters and output data that the authors used for validating the 
algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate 
reproducibility of the reported results.

Image Segmentation

Kenneth Johnson's submission for the Open Source Software Practice course homework. Includes an implementation of Confidence Connected. Brain data, Normal007-T2.mha from MIDAS - Kitware is not included, as I don't have permission to redistribute it.