BioImage Suite is an NIH-supported medical image analysis software suite developed at Yale. It
leverages both the Visualization Toolkit (VTK) and the Insight Toolkit (ITK) and it includes many additional
algorithms for image analysis especially in the areas of segmentation, registration, diffusion
weighted image processing and fMRI analysis. BioImage Suite has a user-friendly user interface developed
in the Tcl scripting language. A final beta version is freely available for download.

BioImage Suite: An integrated medical image analysis suite: An update

This paper describes the Datatree module of BioImage Suite, that allows for rapid and flexible reslicing
of images based on a user-defined tree structure that organizes image and transformation data as well as
electrode localizations, image landmarks, and surface objects. This Datatree tool provides abstracted file
loading, allowing for interface with databases, as well as multisubject capabilities, other plugins designed
for specific image modalities, and an incr Tcl based API, allowing users to extend its functionality with
their own plugins.

The BioImage Suite Datatree Tool: Enabling Flexible Realtime Surgical Visualizations

The software requirements for computer-aided navigation tools in image-guided therapy are becoming increasingly complex as a result of new possibilities in imaging and tracking hardware, novel application areas and regulatory restrictions.
A new software framework has been developed to meet the needs of emerging image-guided procedures and therapies that require multi-modal imaging and tracking. This framework accommodates dynamic data-structures, dynamic input and output interfaces for interventional devices, and dynamic visualization of data. Arbitrary, yet meaningful, connections can be established between these entities in order to implement customized applications that target specific clinical procedures. A series of demonstration applications have been developed in order to emphasize different aspects of the framework, and are presented here.

The SIGN: A dynamic and extensible software framework for Image-Guided Therapy

ImageLib is a free object-oriented development environment destined to biomedical image processing. The software package contains a programming library (C++ language) and an MDI user interface (kernel). The major interest is to allow the creation of fully modular applications. Both user processing functions and image read/write facilities are separately compiled as DLLs, and the resulting plugin objects are dynamically imported into the kernel. The library provides all the required wrapping material to incorporate all find of image processing functions into user-written components. A rapid prototyping application is provided to communicate with the kernel communication interface. No graphics programming skills is required to manage advanced user interactions, image display (e.g. OpenGL), and generic image processing functions based on C++ templates. The component-oriented software architecture makes it possible to share image processing and handling resources for light Windows processing applications.

A programming environment for the development of modular 3D biomedical image processing applications

We describe the implementation of smoke and bleeding simulation in the open source surgery simulator SPRING \cite{montgomery02}, which is particularly targeted for minimally invasive surgery simulation. Many smoke and bleeding simulations offer high physical and visual accuracy, but the underlaying models are to complex to run in real-time while performing soft-tissue simulation, collision detection and haptic device support at the same time. Our algorithms are based on simple models, that allow the surgery simulation to run in real-time.

Real-Time Smoke and Bleeding Simulation in Virtual Surgery

The process of outlining organs on three dimensional medical images is extremely time-consuming. In this document, we describe a new tool that can interpolate contours and also make them smooth. The tool uses geodesic snakes to perform these functions. The implementation uses The Insight Toolkit and
the implementation of geodesic snakes that comes with SNAP.

GeoInterp: Contour Interpolation with Geodesic Snakes
Geodesic Snakes

Shape analysis has become of increasing interest to
the neuroimaging community due to its potential to precisely locate
morphological changes between healthy and pathological structures.
This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology. 

The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling $T^2$ two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.

The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.

<a href="http://www.nitrc.org/projects/spharm-pdm"> New NITRC page for binary distribution </a>
 

Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM

The gestational age (GA) determination is one of the most used measures to guide diagnostics of specialists in neonatology to
guarantee the newborn surveillance. However, the currently available methods have been subject to a number of evaluations and criticism by important medical publications concerning both their low precision and their invasive procedures. Based on this scenario, the FootScanAge method was conceived, seeking to determine the GA through digital imaging of the newborn plantar surface. To support the evaluation and evolution of this new method, it was developed an Open Source and Java based Decision Support System that combines Data Mining and Image Processing techniques to implement the FootScanAge method. The system was developed taking advantage of high degree of interaction between experts in neonatology and computer science.

Applying Image Mining to Support Gestational Age Determination

Image fusion provides a mechanism to combine multiple images into a single representation to aid human visual perception and image processing tasks. Such algorithms endeavour to create a fused image containing the salient information from each source image, without introducing artefacts or inconsistencies. Image fusion is applicable for numerous fields including: defence systems, remote sensing and geoscience, robotics and industrial engineering, and medical imaging. In the medical imaging domain, image fusion may aid diagnosis and surgical planning tasks requiring the segmentation, feature extraction, and/or visualisation of multi-modal datasets.

This paper discusses the implementation of an image fusion toolkit built upon the Insight Toolkit (ITK). Based on an existing architecture, the proposed framework (GIFT) offers a 'plug-and-play' environment for the construction of n-D multi-scale image fusion methods. We give a brief overview of the toolkit design and demonstrate how to construct image fusion algorithms from low-level components (such as multi-scale methods and feature generators). A number of worked examples for medical applications are presented in Appendix A, including quadrature mirror filter discrete wavelet transform (QMF DWT) image fusion.

The Generalised Image Fusion Toolkit (GIFT)

Medical image analysis today is based on serious mathematics. More and more methods involve PDE's, linear algebra,
complex transforms, optimization theory etc. This paper introduces the software environment MathVisionTools that is suited for the design stage, where the emphasis
lies on mathematical modeling. MathVisionTools is written in the high level language Mathematica.