In this work a minimum cost path approach is adopted to extract coronary artery centerlines from CTA data. The algorithm depends on the manual definition of the start and end point of the vessel. The cost image used in the minimal cost path approach is based on a vesselness measure and a smooth window function on intensity. In the majority of the cases the method was able to extract the centerlines successfully (overlap > 90%). Accuracy of the method is around two times the voxelsize of the datasets. To conclude, minimum cost path approaches have potential for coronary artery centerline extraction, but improvements, especially regarding the accuracy of the method, still need investigations.

Two Point Minimum Cost Path Approach for CTA Coronary Centerline Extraction

Several algorithms exist for correcting the nonuniform intensity in magnetic resonance images caused by field inhomogeneities. These algorithms constitute important preprocessing steps for subsequent image analysis tasks. One such algorithm, known as parametric bias field correction (PABIC), has already been implemented in ITK. Another popular algorithm is the nonuniform intensity normalization (N3) approach. A particularly salient advantage of this algorithm is that it does not require a prior tissue model for its application. In addition, the source code for N3 is publicly available at the McConnell Brain Imaging Centre (Montreal Neurological Institute, McGill University) which includes source code and the coordinating set of perl scripts. This submission describes an implementation of the N3 algorithm for the Insight Toolkit given as a single class, viz.	itk::N3MRIBiasFieldCorrectionImageFilter. We tried to maintain minimal difference between the publicly available MNI N3 implementation and our ITK im- plementation. The only intentional variation is the substitution of an earlier contribution, i.e. the class itk::BSplineScatteredDataPointSetToImageFilter, for the originally proposed least-squares approach for B-spline fitting used to model the bias field. In addition, we include a more extensive modification to the original N3 algorithm found in the class itk::N4MRIBiasFieldCorrectionImageFilter. The latter algorithm employs a multi-resolution approach, similar to FFD image registration strategies, and has a slightly modified iterative update scheme.

N4ITK: Nick's N3 ITK Implementation For MRI Bias Field Correction

 Branches of tubular structures (vasculature, trachea, neuron, etc.) in medical images are critical for the topology of these structures. In many applications, It is very helpful to be able to decompose tubular structures and identify every individual branch. For example, quantification of geometric vascular features, registration of trachea movement due to respiration, tracing of neuron path. However, manual decomposition can be tedious, time-consuming, and subject to operator bias. In this paper, we propose a novel method to decompose tubular structures automatically and describe how to implement it in ITK framework. The input is a 2D/3D binary image that can be obtained from any segmentation techniques, as well as the junctions, which can be generated automatically from our previously contributed ITK class: itk::JunctionDetectionFilter. The output will be branches with their labels and their connection. There are only two parameters which need to be set by the user. We provide here the implementation as a ITK class: itk::BranchDecompositionFilter.
Please cite the following paper if you are interested in our work.
G. Xiong, C. Chen, J. Chen, Y. Xie, and L. Xing, Tracking the Motion Trajectories of Junction Structures in 4D CT Images of the Lung, Vol. 57, No. 15, pp. 4905-4930, Physics in Medicine and Biology, 2012.

Automatic Branch Decomposition for Tubular Structures

An Insight Toolkit (ITK) processing framework for segmenting and tracking nuclei in time-lapse microscopy images using coupled active contours is presented in this paper. We implement the method of Dufour et al.[2] to segment and track cells in fluorescence microscopy images. The basic idea is to model the image as a constant intensity background with constant intensity foreground components. We utilizes our earlier submissions on the Chan and Vese algorithm [1] and its multiphase extension [5] to build our new tracking filter. The tracking filter itk::MultiphaseLevelSetTracking inputs a segmentation result (or a coarse estimate) from the previous time-point along with the feature image and generates a new segmentation output. By iteratively repeating this process across all time-points, real-time tracking is made possible. We include 2D/3D example code, parameter settings and show the results generated on a 2D zebrafish embryo image series.

Cell Tracking using Coupled Active Surfaces for Nuclei and Membranes

This document describes an implementation of David S. Tuch\'s numerical Q-Ball reconstruction algorithm implemented using the Insight Toolkit ITK (www.itk.org). The document is accompanied with the source code, input data, parameters and output data that were used for validating the implementation. 

Numerical Q-Ball Image Reconstruction: an ITK Implementation

This article provides an implementation of the symmetric log-domain diffeomorphic image registration algorithm, or symmetric demons algorithm for short. It generalizes Thirion's demons and the diffeomorphic demons algorithm. The main practical advantages of the symmetric demons with respect to the other demons variants is that is provides the inverse of the spatial transformation at no additional computational cost and ensures that the registration of image A to image B provides the inverse of the registration from image B to image A. The algorithm works completely in the log-domain, i.e. it uses a stationary velocity field to encode the spatial transformation as its exponential. Within the Insight Toolkit (ITK), the classical demons algorithm is implemented as part of the finite difference solver framework. Our code reuses and extends this generic framework. The source code is composed of a set of reusable ITK filters and classes together with their unit tests. We also provide a small example program that allows the user to compare the different variants of the demons algorithm. This paper gives an overview of the algorithm, an overview of its implementation and a small user guide to ease the use of the registration executable.

An ITK Implementation of the Symmetric Log-Domain Diffeomorphic Demons Algorithm

This document describes a contribution to the Insight Toolkit intended to support the process of registering two Meshes. The methods included here are restricted to Meshes with a Spherical geometry and topology, and with scalar values associated to their nodes.
This paper is accompanied with the source code, input data, parameters and output data that we used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.

Rotational Registration of Spherical Surfaces Represented as QuadEdge Meshes

By default ITK images use a contiguous memory model. This means pixel elements are stored in a single 1-D array, where each element is adjacent in memory to the previous element. However in some situations it is not desirable to use this memory model. This document describes three alternative memory models: slice contiguous, sparse, and single-bit binary images. Slice contiguous images are three-dimensional images, in which each slice is stored in a contiguous 1-D array, but the slices are not necessarily adjacent in memory. Slice contiguous images are well suited for interoperability with applications representing images using DICOM. Sparse images are n-dimensional images, in which each pixel is stored in a hash table data structure. This memory model is well suited for images with very large dimensions, but few pixels which are actually relevant. Single-bit binary images internally represent each pixel as a single-bit, in contrast to eight-bits required to represent a boolean. Single-bit binary images allow very compact representations for on-off masks. Source code, tests, and examples are provided to allow easy reproduction and use.

Alternative Memory Models for ITK Images

In the Digital Operating Room there is a need to support data streaming to create advanced integrated surgical assist systems. In this paper we propose a DICOM-based streaming mechanism which leverages the interoperability definitions offered by DICOM to offer a common interface to manage all kinds of streaming data sources, while allowing data and application-specific protocols and infrastructure for the actual data access. We have implemented the proposed solution within the ASTMA project and have shown that thanks to the flexibility in choosing an appropriate streaming protocol we can achieve the necessary streaming quality while transmitting the context information required to create valid DICOM instances. This approach ensures an early integration of streaming data with the rest of the imaging information providing for a simpler data workflow.

A DICOM-based streaming service for the Digital Operating Room

This paper presents an original role-based software architecture facilitating the flexible composition, configuration and collaboration of separated components in the field of computer assisted interventions. Roles, which can be seen as methods dynamically attached to objects, are embedded in components, to limit build level dependencies and improve flexibility. An appropriate component definition and composition language is proposed to declare softwares, without any specific initialization or glue code, this remaining a challenging issue in component oriented programming. The potential of this architecture is illustrated for a software coupling electromagnetic tracking with a robotized system dedicated to the physiological motion rejection in flexible endoscopy. This software consists in several independent components with are combined at runtime thanks to a concise XML-based declaration.

A role-based component architecture for computer assisted interventions: illustration for electromagnetic tracking and robotized motion rejection in flexible endoscopy