This paper describes the implementation of a multidimensional block-matching nonrigid registration
algorithm. The main features of the algorithm are its simplicity, its free form nature, the modularity of
the similarity measure, which makes it possible using local entropy-based similarity measures and the
avoidance of the optimization module. The algorithm implementation described in this paper is based
on the method by Suarez et al. This paper, which has already been submitted to the Insight
Journal, is accompanied with the source code, input data, parameters and output data used for validating
the algorithm described in it.

Fast BlockMatching Registration with Entropy-based Similarity

In this paper, we describe a database of cine-MR (3D+t) images of the
left ventricle. This database contains the voxel data, one automated
and two manual segmentations for each sequence of images. The
segmentations are validated from a clinical point of view. We detail
how the images were obtained, as well as how the associated
segmentations were performed. We also provide the data clinical
validation process.

This database, including tools to compute quantitative measures and
the software package used to obtain the automated segmentation, is
freely available for research purposes.

An open, clinically-validated database of 3D+t cine-MR images of the left ventricle with associated manual and automated segmentation

Volumetric layers often encountered in medical image analysis are characterized by double and nested bounding surfaces. The thickness of a volumetric layer at a point on the bounding surface is the distance from that point to the opposite surface. There exist several definitions for the layer thickness. A newly proposed thickness definition is the radial thickness, which is defined as the distance between each pair of corresponding points on the two surfaces with the same polar coordinate. The thickness values calculated by the radial thickness definition are unique and does not depend on the starting surface. In this paper, we describe a class for calculating the radial thickness of one volumetric layer represented as coupled and nested triangle meshes. 

The class, vtkRadialThicknessCalculate, is implemented using the Visualization Toolkit (VTK). In this document, we describe the radial thickness calculation algorithm and provide the user with the source code and the input data to reproduce the results. The radial thickness calculation described in this paper has a variety of applications including the thickness calculation for the skull vault, which is the original motivation for this work.

Radial Thickness Calculation and Visualization for Volumetric Layers

At the Johns Hopkins University’s Engineering Research Center for Computer-Integrated Surgical Systems and Technology (ERC-CISST) laboratory, we have designed and developed a platformindependent C++ software package, called the nArray library, that provides a unified framework for efficiently working with multidimensional data sets. In this paper, we present and discuss the core elements of the library, including its intuitive and uniform API, efficient arithmetic engine algorithm, and efficient sub-volume algorithm. We then compare the performance of the nArray library with that of an existing multidimensional array toolkit, ITK. We conclude that the nArray library is more efficient than ITK in many situations, especially in operations on sub-arrays, and that the two packages have comparable performance in many other scenarios. The underlying algorithms, if incorporated in ITK, can help improve its performance.

Multidimensional Arrays and the nArray Package

Kernel based filtering is one of the fundamental tools of image
analysis and processing. A number of approaches have been developed
over the years that allow efficient implementation of such filters
even when the kernel size is large. This article reviews some of these
methods and introduces their ITK implementations.

Efficient implementation of kernel filtering

This paper describes an image processing prototyping environment called SharpImage. SharpImage is a stand-alone Windows application geared towards the pre-processing and creation of volume illustrations, although other generic tasks are inherently supported. The user interacts with the environment using a command console, and can view images as a series of slices or through volume rendering. We describe the design and introduce functionality using a number of examples. We have found the environment useful for prototyping a range of image processing tasks and provide the source-code in the hope others may also find it useful.

SharpImage: An Image Processing Prototyping Environment

This is a companion paper describing the process and parameters for tagged volume rendering of the heart. We firstly review the relevant concepts and consider the problem of visualising the coronary arteries from computed tomography angiography (CTA) images. We then discuss the implementation using the SharpImage prototyping environment. Finally, we list the set of commands capable of reproducing our results using the accompanying dataset.

Tagged Volume Rendering of the Heart: A Case Study

This paper describes vessel enhancing diffusion (VED) filters implemented 
using the Insight Toolkit (ITK). The filters are implementation of the 
VED algorithm developed by Manniesing et al . The VED algorithm follows a 
multiscale approach to enhance vessels using anisotropic diffusion scheme guided 
by vesselness measure at a pixel level. Vesselness is determined by geometrical 
analysis of the Eigen system of the Hessian matrix. For this purpose, a smoothed version of the Frangi's 
vesselness function is formulated. Experiments were conducted to evaluate the performance
of the VED filters in enhancing vessels in lung CT scan.

Vessel Enhancing Diffusion Filter

This paper presents an extension of the Visualization ToolKit dedicated to spatiotemporal data synchronization, visualization and management. It basically consists in a versatile library providing functionalities to help developers setting up sophisticated applications with minimal development effort. In the medical imaging context, various types of data are often encountered, which raises the need for adapted visualization and synchronization techniques. Moreover, the management of these data (organization, creation, deletion, access) can become a burden. We propose in vtkINRIA3D a strategy to synchronize interactions between datasets representations, to manipulate complex objects (e.g., neural fibers as obtained in DT-MRI), as well as a managing framework for organizing data (including temporal sequences). The efficiency of vtkINRIA3D is illustrated with two applications: MedINRIA (general medical image processing software) and CardioViz3D (cardiac image visualization). vtkINRIA3D is open-source, and comes with a set of examples, test data and softwares built upon it: http://www-sop.inria.fr/asclepios/software/vtkINRIA3D.

vtkINRIA3D: A VTK Extension for Spatiotemporal Data Synchronization, Visualization and Management

In this document it is described the implementation of the flux driven automatic centerline extraction
algorithm proposed by Bouix et al. in 2004. This is based on a skeletonisation algorithm initially
proposed by Siddiqi et al. in 2002, using properties of an average outward flux measure to distinguish
skeletal points from non-skeletal ones. This information is combined with a topology preserving thinning
procedure to obtain the final result. This implementation combines this skeletonisation algorithm with
other techniques as described in the paper of Bouix et al. to produce an ITK filter that generates as output
the skeleton, as a binary object represented in an image, of the input surface, represented as a distance
transform image. In this work is described this medial curve extraction procedure following the ITK
philosophy.