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.

MathVisionTools: mathematical prototyping in medical image analysis

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