Many statistical shape analysis methods produce various types of data about the analyzed surfaces, such
as p-value maps, distance maps, 3D difference vectors and local covariance matrices. This data is often
too large and thus difficult to be properly evaluated on a qualitative basis. A visual representation of
this data strongly simplifies qualitative evaluation by humans and thus greatly enhances the value of
the statistical results. In this paper we present a new tool for visualizing various datasets on surfaces
represented as triangle meshes.
Our tool, KWMeshVisu, is implemented using the Insight Toolkit ITK, www.itk.org, the Visualization
Toolkit VTK, www.vtk.org, and the KWWidgets user interface toolkit, www.kwwidgets.org.
The source code for KWMeshVisu, as well as input data used to generate the images in this paper, is
provided with this document.

KWMeshVisu: A Mesh Visualization Tool for Shape Analysis

In this work a software framework for the virtual surgical simulation is presented. 
Our environment ESQUI (The acronym comes from the Spanish "Entorno para Simulacion QUIrurgica", i.e.
Framework for Surgical Simulation) is based on an open software and multi-platform architecture (Unix, 
Windows, etc.), resulting in a suitable framework for research and development. The modularity and connectivity 
between its components allow the environment to be extensible and adaptable to any type of virtual simulation. 
The virtual surgical scene is described by an XML file that contains all the necessary information for the 
simulation. 
This file description language has been named Surgical Reality Modeling Language (SRML). 
Although our framework is still under development and needs validation in education and clinical training, 
our environment ESQUI has proved to be a modular platform that runs across most important areas of virtual 
surgical simulation.

An Open Source Framework for Surgical Simulation

The Lung Tissue Research Consortium (LTRC) is an NHLBI sponsored project which is studying histological, radiological, and clinical data on patients with Chronic Obstructive Pulmonary Disease (COPD) and idiopathic pulmonary fibrosis (IPF). At completion, over 1200 subjects will be enrolled. Each subject will have a minimum of one CT scan; however, many will likely have 3 CT scans. Each scan will have a corresponding radiological report, clinical report, and histological report. The NHLBI requires that the data collected through the LTRC be anonymized and available to independent researchers. The LTRC imaging database will be the most comprehensive database of lung CT data available to date and will serve as the basis for imaging studies of chronic lung disease for years to come. It is a significant opportunity for the medical image analysis community to investigate this database with the intent to develop new methods of registration, segmentation, and classification that may be used to diagnosis and treat patients with diffuse lung disease.

The Lung Tissue Research Consortium: An extensive open database containing histological, clinical, and radiological data to study chronic lung disease

Using a robot system to position needles or needle shaped tools during clinical procedures such as biopsy, radio frequency ablation, and target drug delivery has a great potentials in increasing accuracy and speed of the process, and minimizing trauma to patient. This paper describes a robot assist needle placement system developed using Image Guided Surgery Toolkit (IGSTK). IGSTK is an open source software toolkit aimed at providing a robust and safe platform for researchers and clinicians for fast prototyping of image guided applications with minimum cost and effort

Robot Assisted Needle Placement: Application developed using an open source image guided surgery toolkit (IGSTK)

Statistical Shape Models are a popular method for segmenting three-dimensional medical images. To
obtain the required landmark correspondences, various automatic approaches have been proposed. In
this work, we present an improved version of minimizing the description length (MDL) of the model. To
initialize the algorithm, we describe a method to distribute landmarks on the training shapes using a conformal
parameterization function. Then, we introduce a novel procedure to modify landmark positions
locally without disturbing established correspondences. We employ a gradient descent optimization to
minimize the MDL cost function, speeding up automatic model building by several orders of magnitude
when compared to the original MDL approach. The necessary gradient information is estimated from
a singular value decomposition, a more accurate technique to calculate the PCA than the commonly
used eigendecomposition of the covariance matrix. In this work, we first present a basic version where
spatial locations are used in the MDL cost function; next, we introduce an extended version where any
combination of features can be used as a metric. As an example application, we present results based on
local curvature measurements. Finally, we present results for synthetic and real-world datasets demonstrating
the efficiency of our procedures and give details about the implementation using the Insight
Toolkit (ITK).

Implementing the Automatic Generation of 3D Statistical Shape Models with ITK

This paper describes the Insight Toolkit (ITK) Conformal Flattening filter: itkConformalFlatteningFilter.
This ITK filter is an implementation of a paper by Sigurd Angenent, et al., “On the Laplace-Beltrami
Operator and Brain Surface Flattening”. This filter performs an angle preserving map of any genus
zero (i.e. no handles) triangulated mesh to the sphere or, alternatively, to the plane. In other words,
any given triangle in the resulting sphere will have the same angles as the corresponding triangle in the
original mesh, though their areas may differ. In this paper, we describe our code and provide the user
with enough details to reproduce the results which we present in this paper. This filter has a variety of
applications including the flattening of brain surfaces, which was the initial motivation for this work.

Conformal Flattening ITK Filter

The current conversion from an ellipse spatial object to an image
using SpatialObjectToImageFilter filter could cost minutes because
the filter traverses all pixels in the output image. This delay is
quite noticeable during applications such as human-machine
interface. This document describes a contribution to the Insight
Toolkit ITK www.itk.org, which implements what we call an
itk::EllipseBoundaryToImageFilter. This is a derived from
SpatialObjectToImageFilter. It marks the output image by setting
the pixels along the boundary of the input ellipse to the
InsideValue and the OutsideValue elsewhere. This filter is fast
because it traverses the boundary only. This paper is accompanied
with the source code.

itkEllipseBoundaryToImageFilter

This paper describes the use of open source software in the development and testing of an image-guided robot system for small animal research, presented at MICCAI 2006. This system relied on a significant amount of open source software, including 3D Slicer,
VTK, our own cisst software, the NetLib numerical methods, Python, and wxPython
(which uses wxWidgets). In addition, several open source development tools were used, including CVS, CMake, and Swig. The paper will be accompanied by the source code and raw data that were used to obtain the results presented at MICCAI.

Open Source Software in the Development and Testing of an Image-Guided Robot System

Medical image analysis is an important problem relating to
the study of various diseases. Since their inception to MICCAI in
2001, ''deformable organisms'' have emerged as a fruitful
methodology with examples ranging from 2D corpus callosum
segmentation to 3D vasculature and spinal cord segmentation.
Essentially we have developed an artificial life framework that
complements classical deformable models (snakes and deformable
meshes) with high-level, anatomically-driven control mechanisms.
This paper describes the integration of deformable organisms
into the Insight Toolkit (ITK) \url{www.itk.org}. Our code attempts
to bridge the ITK framework and coding style with deformable organism
design methodologies. In the interest of open science, as the
framework develops it will serve as a basis for the community to
develop new deformable organisms as well as experiment with those
recently published by our group.

I-DO: A Deformable Organisms framework for ITK

This article presents an approach for decreasing the computational time for converting a sparse SpatialObject to an Image. The method is applicable for SpatialObjects which occupy less volume than the total output Image (which occurs often, especially when using TubeSpatialObjects to represent vessels). A new filter MaskedSpatialObjectToImageFilter is introduced which dramatically decreases the execution time by creating a mask of relevant pixels. The mask is computed by calling the conventional SpatialObjectToImageFilter at a lower resolution, which is significantly faster for large images. The mask is then up-sampled to control which areas are further processed.