ModulusImageFilter is a simple image filter to compute the modulus (x % dividend) pixel-wise.

ModulusImageFilter

A report on the issue of image orientation with respect to the Insight Toolkit. A description of how orientation is defined in ITK, and the tools available for manipulating and detecting image orientation.

A definition of Spatial Orientation for the Insight Toolkit

A filter to impose to the input image the minima defined in the marker image.

MinimaImpositionImageFilter

MINC2.0 IO Support for ITK

Finding regional extrema of images is an important step in a number of
morphological algorithms, such as some versions of the watershed
transform\footnote{This work started because we were trying to compare
different watershed algorithms}. Regional extrema may also be
important cues of other tasks, such as splitting objects based on
distance transform information. This report provides an overview of
the methods available in ITK and compares the performance with a new
filter.

Finding regional extrema - methods and performance

To facilitate the analysis of medical image data in research environment, MATITK is developed to allow ITK algorithms to be called in MATLAB. ITK is a powerful open-source image analysis toolkit, but it requires the knowledge of C++ to use it. With the help of MATITK, researchers familiar with MATLAB can harness the power of ITK without learning C++ and worrying about low-level programming issues. A common set of C++ classes have also been produced to allow future ITK methods to be added to MATITK therefore callable in MATLAB without the bothersome translation between MATLAB and ITK.

MATITK: EXTENDING MATLAB WITH ITK

ImageCalculator is a command line program for doing simple mathematical operations to a series of images and generating an new output image and basic statistics about the output image. The program uses the Insight Toolkit (www.ITK.org) for file IO and computations, and can operate on any of the image
types supported by that library.

Image Calculator

Probability distributions are a key component of clinical research. One only has to make a cursory review of clinical literature to realize that nearly all clinical publications reference some sort of statistical test; be it t-test, chi-squared test, or F-test. In this paper, we describe an architecture for providing the Insight Toolkit with access to probability distributions. The architecture can support parametric and nonparametric probability distributions. Each distribution provides access to its probability density function (PDF), cumulative distribution function (CDF), inverse cumulative distribution function (inverse CDF), mean, and variance. These methods form the basis of statistical tests.

Probability Distributions for the Insight Toolkit

An Insight Toolkit (ITK) filter for image segmentation with
applications to brain MRI scans is presented in this paper. Previously, we
showed how ITK could be used to implement our algorithm. This paper
presents our new ITK filter for Bayesian segmentation along with results
on brain MRI scans. Our algorithm is a refinement of the work of Teo,
Saprio, and Wandall. The basic idea is to incorporate prior knowledge
into the segmentation through Bayes rule. Image noise is removed via an
affine invariant anisotropic smoothing of the posteriors as in Haker et. al.
Specifically, we present the implementation of our Bayesian segmentation
algorithm applied to brain MRI scans.

An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter

This course introduces attendees to select open-source efforts in the field of medical image analysis. Opportunities for users and developers are presented. 
	The course particularly focuses on the open-source Insight Toolkit (ITK) for medical image segmentation and registration. The course describes the procedure for downloading and installing the toolkit and covers the use of its data representation and filtering classes. Attendees are shown how ITK can be used in their research, rapid prototyping, and application development.

LEARNING OUTCOMES
After completing this course, attendees will be able to:
contribute to and benefit from open-source software for medical image analysis
download and install the ITK toolkit 
start their own software project based on ITK 
design and construct an image processing pipeline 
combine ITK filters for medical image segmentation 
combine ITK components for medical image registration 

INTENDED AUDIENCE
This course is intended for anyone involved in medical image analysis. In particular it targets graduate students, researchers and professionals in the areas of computer science and medicine. Attendees should have an intermediate level on object oriented programming with C++ and must be familiar with the basics of medical image processing and analysis.