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

Confidence Connected Segmentation using ITK

This document describes an implementation of Connected Threshold Segmentation to isolate the ventricles using Insight Toolkit www.itk.org.

Connected Threshold Segmentation of Ventricles in the Brain

This document describes the process used in implementing image segmentation through a region growing algorithm with the use of the Insight Toolkit (ITK) imaging program. The code used to implement the region growing algorithm is documented in the ITK Software Guide and makes use of the ITK library. This paper is accompanied with all source code, input data, parameters and output data that were originally used in developing the image segmentation application, except where prior laws or agreements prohibit redistribution of such material. The only data subject to not being redistributable was the input file used for testing which was a brain dataset from Kitware’s MIDAS collection named Normal090-T2.mha. The rest of this scientific publication is written in a manner such that it facilitates reproducibility of the reported results.

Image Segmentation: Ventricle Isolation Using Region Growing

ITK includes a number of iterators including the {\em itkImageLinearConstIteratorWithIndex} and {\em
itkImageLinearIteratorWithIndex} that support visiting voxels along
lines parallel to the image axes. This is particularly useful for
separable filters. This article introduces modified versions that do
not maintain location index so that filters that don't require index
information can perform faster.

Preliminary tests indicate that the iteration speed is improved, but
that filters such as the RecursiveSmoothingGaussianImageFilter do not
change significantly.

ImageLinearIterator - WithIndex removed

This document describes some experiments for segmentation using the Insight Toolkit
ITK \url{www.itk.org}. The code of the experiment is written following the
ITK CodingStyle as described in the directory \code{Insight/Documentation/Style.pdf}.

Segmentation is still an open problem and there are quite a lot of algorithms 
specific to application domains and data being used. In this experiment I've used 
a 3-D MRA scan of Brain retrieved from MIDAS website. This paper is accompanied 
with the source code, input data, parameters and output data that the author used for experimenting with the algorithm.

Experimenting with Segmentation in ITK

This paper describes the use of the Insight Toolkit (www.itk.org) to segment the ventricles in MR image data obtained from the Designed Database of MR Brain Images of Healthy Volunteers hosted at the MIDAS project.

Ventricle Segmentation with Itk's Watershed filter

Parameterization of surfaces, sometimes alled ”‘flattening”’ as it maps a surface embedded in 3D into its intrinsic 2D domain, is a powerfull tool for the analysis of surfaces. For the past years, there has been a growing interest in the Community that even lead to one implementation of one special type of parameterization in ITK [1,5,8,9]. We are providing here a more general framework for parameterization of single connected surfaces of any genus. It is based on a recent addition to ITK: itkQuadEdgeMesh [6] which allows an elegant an optimal implementation of algorithm for geometry and topology processing of discrete 2-manifolds. The 5 algorithms that we implemented map the meshes into a planar domain with fixed boundary leading to more stability and speed than mapping into the spherical domain. Each of them use different kind of parameterization with different properties. The conformal parameterization is usually used as it is intrinsic to the geometry of the mesh and thus allow shape analysis independently of the connectivity. However if flattening the mesh is your only goal, then the simplest parameterization algorithm (graph theory) will give you the best speed. This is the case when you want to further process the mesh in a lower dimension. Using specific solvers, the parameterization of meshes can be done sufficiently fast (couple of seconds) to consider this approach in interactive applications.