Dynamic magnetic resonance imaging (DCE-MRI) carried out with
contrast media such as Gd-chelate complex (Gd-DTPA) allows the
non-invasive assessment of microcirculatory characteristics of
malignant lesions. Quantitative estimation of lesion parameters
from the passage of the contrast media requires the use of
pharmacokinetic two-compartment model. The input to the model is
the time-intensity plot from a region of interest (ROI) covering
the lesion extent. The lengthy imaging process, elasticity of the
organs and patient movement result in complex deformations in the
subject requiring 3D motion correction for ROI alignment. This
paper presents results on applying the Thirion Demon's 3D elastic
matching procedure in the ITK framework on the two-compartment
lesion parameters. Registration, meanwhile involves interpolation
and smoothing operations thereby affecting the time-intensity
plots. We explore the trade-offs that arise between registration
and lesion parameter estimation. Experiments on synthesized and
real deformation are presented.

Impact of motion correction on the quantitative analysis of DCE-MR Images

The gold standard for measuring muscle regeneration in muscular dystrophy therapies is counting the number of dystrophin-positive muscle fibers on a cryostat muscle section immunostained for dystrophin. The standard process of manually counting a few thousand myofibers is tedious, time consuming, and limits quantitative analysis of a therapy success. We present an unsupervised method for segmenting and counting the number of myofibers on an immunofluorescent microscopy image. The key threshold selection problem is resolved by maximizing the number of sub-threshold connected components. Components significantly smaller than the known lower bound myofiber area, the only input parameter, are ignored to reduce noise. Validation on a series of images (n=63) revealed that our algorithm varied by less than 10% from manual counts in the relevant range of operation. The algorithm allows us to quantify three-dimensional dystrophin expression and design experiments that address a major limitation in muscular dystrophy therapies, the limited distribution of dystrophin after treatment. Further we have extended this method to segment and count objects in other immunofluorescent images. The method was quickly developed and tested using the Insight Toolkit (ITK), an open source C++ library for the development of image analysis software.

Unsupervised Segmentation for Myofiber Counting in Immunofluorescent Microscopy Images

We are developing open source software for computer assisted intervention systems. Our primary experience has been with medical
robots, but the concepts (and software) apply to many physical devices that interact with the real world.
The real-time performance requirements permeate all levels of our software, including common tools (such as logging, class and object registers),
vectors, matrices and transformations. Our software libraries are written in C++, but are also accessible from Python, which
provides a convenient environment for rapid prototyping and interactive testing.
The real-time support includes a device (hardware) interface and a task library. Device-specific modules such as robot servo control and trajectory generation can be provided by tasks or by external devices.
Ultimately, we intend to provide a framework that supports extension via dynamically loaded plug-in modules.
Our development process utilizes a multitude of open source tools, including CVS, CMake, Swig, CppUnit, Dart, CVSTrac, Doxygen
and LaTeX. These tools help to ensure compliance with our software development procedure.

Development of open source software for computer-assisted intervention systems

Salient points are used for various applications, such as medical image registration, tracking, stereoscopic matching. The purpose of this paper is to compare two commonly used methods to extract salient points in 3D medical images. We give an interpretation of the methods and validate their performance empirically based on criteria derived for the task of image registration, displacement measurement and tracking in medical images.

Comparison of Salient Point Detection Methods for 3D Medical Images

Isosurface extraction from brain images often creates handles between brain folds that are anatomically separated. Although manual editing could optimally correct these anatomical errors, it is not realistic due the size of the 3D data and the convoluted geometry of the brain. We propose an algorithm to automatically repair the isosurface and we make our code available at http://www.OpenTopology.org

Open Topology: A Toolkit for Brain Isosurface Correction

OpenTissue is a multidisciplinary, open source programming toolkit
 for physics-based simulation, collision detection, scientific
 visualization, and medical imaging. The toolkit was initiated in
 2001, is driven by academia, and is used by several universities.
 It implements several cutting edge simulators using cutting edge
 programming techniques. OpenTissue is available under Windows and
 Linux, and it implements almost all algorithms presented in the
 accompanying book, Erleben et al. Physics-Based Animation, Charles River Media, 2005. This
 presentation will give an overview of OpenTissue: what it can do,
 how it is done, and where the toolkit is going

OpenTissue - An Open Source Toolkit for Physics-Based Animation

Since the 1970's B-splines have evolved to become the {\em de facto} 
standard for curve and surface representation due to many
of their salient properties. Conventional least-squares 
scattered data fitting techniques for B-splines require the inversion 
of potentially large matrices. This is time-consuming as well 
as susceptible to ill-conditioning which leads to undesired
results. Lee {\em et al.} proposed a novel B-spline 
algorithm for fitting a 2-D cubic B-spline surface to scattered 
data in \cite{Lee}. The proposed algorithm utilizes an 
optional multilevel approach for better fitting results. 
We generalize this technique to support N-dimensional 
data fitting as well as arbitrary degree of B-spline. In addition, 
we generalize the B-spline kernel function class to accommodate 
this new image filter.

N-D $C^k$ B-Spline Scattered Data Approximation

Binary morphological closing and opening image filters remove structures smaller than the structuring element in a binary image.

Binary morphological closing and opening image filters

InvertIntensityImageFilter is a convenient filter to invert the intensity of an image.

InvertIntensityImageFilter

Morphological gradients enhance the variation of pixel intensity in a given neighborhood.