Inactivation of the retinoblastoma gene in mouse embryos results
in morphological changes in the placenta, which has been shown to
affect fetal survivability. The construction of a 3D virtual
placenta aids in accurately quantifying structural changes using
image analysis. The placenta dataset consisted of 786 images
totaling 550 GB in size, which were registered into a volumetric
dataset using ITK's registration framework. The registration
process faces many challenges arising from the large image sizes,
damages during sectioning, staining gradients both within and
across sections, and background noise leading to local solutions.
In this work, we implement a rigorous ITK-based preprocessing
pipeline for removing noise and employ a novel 2-level
optimization strategy for enhanced registration in ITK. We provide
3D visualizations and numerical results to demonstrate our
improvements.

ITK-based Registration of Large Images from Light Microscopy: A Biomedical Application

Visualizing metabolic phenotypes, tracking phenotype dynamics, and screening molecular interventions all require comprehensive,
quantitative measures of micromolecular patterns across cell classes. In this project we develop an easy-to-use interface that permits the same sequence of operations as described in Marc and Jones [1] and is based on Cell Kit developed by Marc's lab. Work products for this project include: user application software (Matlab), user manual and if possible, a web portal interface to allow internet access and application of the system. The MORPHIAS system will be implemented by means of a Matlab function which creates a GUI which allows the user to import molecular signature images (in TIFF format), and then to perform classification using a clustering algorithm, and to then perform data analysis on the result.

MORPHIAS: Molecular Phenotyping Image Analysis System

Open source technologies are increasing in popularity for software development. Many open source projects rely on skilled development teams whose members are distributed throughout the world. Often, agile development methods are employed by these teams, as the focus is on concurrent development and fast production over requirements management and quality assurance. The image-guided surgery toolkit (IGSTK) is an open source development project that relies on the collaboration of a skilled and distributed development team, yet addresses a domain that demands managing requirements as well as implementing a high degree of robustness and addressing safety concerns. Due to this unique cross-section of open source technology and the surgical domain, the IGSTK team has developed a set of best practices and requirements techniques to augment commonly applied agile methods. This paper presents the lessons we have learned as we have engaged in the software development process.

IGSTK: Development Process and Project Management Best Practices for an Open Source Software Toolkit for Image-Guided Surgery Applications

The aim of this paper is to present the integration of new filters computing fuzzy mathematical morphology and spatial relationships into ITK and to share our experience with the Open source community. 
Our implementation architecture follows the essential system concepts of ITK. An illustrative application is provided to help ITK users and developers to evaluate the capabilities of these filters.

Integration of Fuzzy Mathematical Morphology and Fuzzy Spatial Relationships into ITK

This paper gives an overview of the use and development of the TINA open-source medical image analysis environment, with respect to the determination of human cerebral cortical thickness estimation from magnetic resonance images. The ultimate aim of TINA is to provide a validated system where the source code and datasets are freely available in order to allow peer-validation of published results.

Cerebral Cortical Thickness Estimation using the TINA
 Open-Source Image Analysis Environment

This paper focuses on the role of open-source software in the development of a novel magnetic resonance image (MRI) tissue classification algorithm. Specifically, we describe the of use existing classes in the Insight Segmentation and Registration Toolkit (ITK) and several new classes that were implemented to perform non-parametric density estimation and entropy minimization. These new classes also provide a general framework for nonparametric density estimation and related applications.

A nonparametric, entropy-minimizing MRI tissue classification algorithm implementation using ITK

In this paper , we propose an extension to the National Library of Medicine's Insight Segmentation and Registration Toolkit (ITK. www.itk.org) with a companion image access and archival system that merges the image transfer capabilities of DICOM with DSpace (www.dspace.org), an open-source web-based digital library technology. We have extended and linked existing, freely-available DICOM and digital library software to create DICOM Accessible Digital Libraries (DADLs). Specifically, DICOM objects (and other medical image data formats such at Analyze,MetaImage) uploaded to a DADL can be grouped with other files such as journal articles or tracker data, viewed as thumbnails, and made publicly or privately available for others to search and download from a linked DICOM server. In that manner, once a researcher has identified the data he / she needs via an Internet search using meaningful descriptors, the data can be directly downloaded via standard DICOM query and retrieve utilities and that data will be perpetually indexed and accessible via an Internet handle.

A DICOM-based Software Infrastructure for Data Archiving

The Image-Guided Surgery Toolkit (IGSTK) is an Open Source software project being developed under NIH/NIBIB funding. The toolkit will provide a common platform for implementing image-guided surgery applications and for fostering research in the field. The toolkit is based on several other open source toolkits including ITK, VTK and FLTK. Its architecture is based on the use of medium size classes, each one of them containing an explicit State Machine and a minimized API that enforces fault tolerance by design. This paper describes the architecture and its rationale.

IGSTK: A State Machine Architecture for an Open Source Software Toolkit for Image-Guided Surgery Applications

Mathematical models are often used to describe natural phenomena, organisms and anatomy. This paper presents a Model-Image Registration framework that can be used to evaluate models of anatomical shape. An optimisation process is applied to the model parameters to fit the model to an organ of interest in a volumetric image. The fit is obtained by maximising a metric that measures how closely the model surface atches the image edge features, according to gradient magnitude and orientation with respect to the surface normal. The system was tested using a spiral shell model, with generated data as ground truth, and also with CT scans of the temporal bone. The parameters converge to within a close tolerance after a few hundred iterations on the test data, and show promising results on registering with the clinical data.

Model-Image Registration of Parametric Shape Models: Fitting a Shell to the Cochlea

We propose a novel framework at MICCAI 2005 to predict pacing sites in the left ventricle (LV) of a heart. This framework can be used to assist pacemaker implantation and programming in cardiac resynchronization therapy (CRT) that is a widely adopted therapy for heart failure patients. Hierarchical agglomerative clustering technique is performed to the time series of LV wall thickness to identify pacing site candidates. Meanwhile, pearson correlation coefficients of wall motion series show the dissimilarity between them. These main components of our clustering based prediction framework are implemented by using open source software toolkit PRTools.