We describe the open source implementation of an adaptive tetrahedral mesh generator particularly targeted for non-rigid FEM registration of MR images. While many medical imaging applications require robust mesh generation, there are few codes available. Moreover, most of the practical implementations are commercial. The algorithm we have implemented has been previously evaluated for simulations of highly deformable objects, and the preliminary results show its applicability to the targeted application. The implementation we describe is open source and will be available within Insight Toolkit.

Tetrahedral mesh generation for medical imaging

BioImage Suite is an integrated image analysis software suite
developed at Yale. It uses a combination of C++ and Tcl in the same
fashion as that pioneered by the Visualization Toolkit (VTK) and it
leverages both VTK and the Insight Toolkit. It has extensive capabilities
for both neuro/cardiac and abdominal image analysis and state of the
art visualization. It is currently in use at Yale; a first public release is
expected before the end of 2005.

BioImage Suite: An integrated medical image analysis suite

Camino is an open-source, object-oriented software package for processing diffusion MRI data. Camino implements a data processing pipeline, which allows for easy scripting and flexible integration with other software. This paper summarises the features of Camino at each stage of the pipeline from the raw data to the statistics used by clinicians and researchers. The paper also discusses the role of Camino in the paper "An Automated Approach to Connectivity-based Partitioning of Brain Structures", published at MICCAI 2005.

Camino: Diffusion MRI reconstruction and processing

A hierarchy of path data types and basic path filters were added to ITK, providing a general framework for curves that map a scalar value to a point in n-dimensional space. The framework supports curves that are either continuous (parametric curves) or discrete (chain-codes). Example usage of the entire framework is demonstrated using a previously published 2D active contour algorithm that was converted to ITK.

Creation and Demonstration of a Framework for Handling Paths in ITK

Diffusion tensor imaging (DTI) allows a quantitative study of the
underlying geometry and architecture of white matter. We have
developed a suite of open source tools for quantitative analysis of
DTI suitable for use in clinical research studies. We have leveraged
open source tools for file IO, image processing, visualization, and
user interface. The tools are designed to provide several
interconnected modules to perform quantitative analysis of DTI data
based on fiber tracts generated from tractography.

A Quantitative DTI Fiber Tract Analysis Suite

This paper investigates an Open Framework for Medical Simulation (named SOFA for Simulation Open Framework Architecture), and proposes ideas for structuring and supporting its expansion. Lately, through a joint effort between the Sim Group at CIMIT and the Alcove group at INRIA, we have investigated the foundations for a more extensive, flexible, and comprehensive framework. In addition, five internationally renowned research groups have contributed to this initiative by converting their algorithms into compatible modules. Although still in its very early developmental phase, the current project illustrates some of the key concepts we believe will enable collaboration and interoperability in Medical Simulation. The main objective of SOFA research is to foster collaboration among research groups. It is our hope that SOFA will simplify the development cycles, reduce production costs, and provide a means to share components through a common interface.

Collaborative Development of an open framework for medical simulation

An Insight Toolkit (ITK) implementation of our knowledgebased
segmentation algorithm applied to brain MRI scans is presented
in this paper. 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 Bayesrule. Image noise is removed via an affine
invariant anisotropic smoothing of the posteriors as in Haker et. al.
We present the results of this code on two different projects. First, we
show the effect of applying this code to skull-removed brain MRI scans.
Second, we show the effect of applying this code to the extraction of the
DLPFC from a user-defined subregion of brain MRI data.We present our
results on brain MRI scans, comparing the results of the knowledge-based
segmentation to manual segmentations on datasets of schizophrenic patients.

Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit

We have successfully created a software environment in which ultrasound data can be manipulated by, ITK (the Insight Tool-Kit), in real-time. We were able to access each frame generated within the resident computer of a TerasonTM Ultrasound Machine, convert it into the ITK image format, and demonstrate the concurrent operation of ITK on the same computer by writing the images to an external hard drive. At a rate of 10 frames per second, 512 by 512 pixel grayscale frames were written by ITK methods to the external hard drive through USB 2.0 while the ultrasound scan was occurring without thrashing or delay in system performance. This simple exercise demonstrates the potential of ITK in processing ultrasound images in real-time in addition to the more traditional off-line processing.

Real-Time Ultrasound Image Analysis for the Insight Toolkit

We have previously developed an algorithm for locating boundaries in an image with sub-pixel resolution, as well as estimating boundary width and image intensity within the adjoining objects. The algorithm operates by finding the parameters of a cumulative Gaussian curve that best approximates an intensity profile taken across a boundary. If intensity is sampled along the image gradient across a boundary, it is reasonable to assume the profile approximates a finite portion of a cumulative Gaussian. Given that assumption, the first derivative of the profile should be the corresponding portion of a Gaussian, completely described by its mean, standard deviation, and amplitude. We present here a simple and rapid method to find those parameters, given that we only have a potentially skewed sample of the Gaussian. The parameters are approximated first for the finite sample, and then both ends of the Gaussian are extrapolated using the resulting parameters. New parameters are then calculated and the procedure is repeated. The optimization rapidly converges, yielding boundary location (mean) with sub-pixel accuracy as well boundary width (standard deviation). Integration then reproduces the cumulative Gaussian, and a least-squares fit is applied to estimate the constant of integration, from which intensity of the adjoining regions can be estimated.

Cumulative Gaussian Curve Fitter for Boundary Parameterization

Using the path framework we previously added to ITK, we implemented a novel algorithm for n-dimensional path optimization, which we call the ND Swath (NDS). NDS uses dynamic programming to globally optimize the placement of a path within an image, subject to several constraints and a user-supplied merit function. The NDS algorithm is presented in this paper along with a description of how it was implemented using ITK.