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.

Kenneth Johnson - Confidence Connected HW

This document describes a tiny application developed using the Insight Toolkit, ITK www.itk.org. The goal of the document is to facilitate ease of use of the application by following the guidelines given in the document. The application does not claim to achieve anything more than the segmentation of the data given with this submission. 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.

Confidence connected segmentation

This document describes implementation of two Level-set segmentation algorithms using Insight Toolkit
ITK . The algorithms chosen for implementation are are 1) Geodesic Active contour Levelset segmentation and 2) Shape Detection Levelset segmentation. The project is oriented to expose the concepts of Open Data, Open Source and Open Access which form the pillars of open-source software ideology.

Image Segmentation Project - Open source software practices class

This document describes an example implementation of segmenting the ventricles of the brain using the Insight Toolkit. Ventricle segmentation is done using the simple confidence connected region growing algorithm.

Included with this paper is the source code, input data, parameters and output data used to validate the implementation described in this paper.

Segmentation Using a Region Growing Algorithm

This document describes a simple method of performing confidence connected segmentation 
using the Insight toolkit (www.itk.org) on data retrieved from the Designed Database of MR 
Brain Images of Healthy Volunteers from MIDAS