MevisLab [2] is a development environment for medical image processing and visualization, which supports the reading and writing of combined dicom/tiff images. In this document we provide the source code (ImageIO factory) for the Insight Toolkit (ITK) framework [4]. This image IO plugin supports 2D, 3D and 4D scalar images.

Note: this class depends on version GDCM 2.0 and above.

Read and Write Support for MevisLab Dicom/Tiff Format

In this paper, a software application is introduced for image registration. BrainDemonsWarp is a com- 
mand line program using Thirion and diffeomorphic demons algorithms. The program takes in a template 
image and a target image along with other optional parameters and registers the template image onto the 
target image. The resultant deformation ﬁelds and metric values can be written to a ﬁle. The program 
uses the Insight Toolkit (www.ITK.org) for all the computations, and can operate on any of the image 
types supported by that library.

BRAINSDemonWarp: An Applicaton to Perform Demons Registration

This paper describes the implementation of a surface smoothing filter in ITK, based ont he Quad Edge Mesh surface data structure.

Surface Meshes Smoothing

Recently, an implementation of the Vessel Enhancing Diffusion (VED) algorithm [3] using the Insight Toolkit (ITK) framework [2] has been proposed by Enquobahrie et al.[1]. In this paper we present an alternative implementation, for two reasons. First, in this implementation all the main functionality of the algorithm, including eigensystem, vesselness, tensor calculation and PDE discretization using a forward Euler scheme are now grouped together in one single class. Although this may come at the cost of code-reusability, it improves readibility and enables application specific code optimization. The second reason is the criterion of reproducibility. Source code, test environment and example data of the paper [3] are provided.


Vessel Enhancing Diffusion

This paper describes the ITK implementation of a shortest path extraction algorithm based on graph representation of the image and the Dijkstra shortest path algorithm. The method requires the user to provide two inputs: 1. path information in the form of start, end, and neighboring mode, the form of which path is allowed to propagate between neighboring pixels, and 2. a weighting function which sets the distance metric between neighboring pixels. A number of perspectives for choosing weighting functions are given, as well as examples using real images. This paper can also serve as an example for utilizing the Boost C++ graph library into the ITK framework.

Implementation of weighted Dijkstra’s shortest-path algorithm for n-D images

This paper presents an ITK implementation for exporting the contours of the automated segmentation results to DICOM-RT Structure Set format. The “radiotherapy structure set” (RTSTRUCT) object of the DICOM standard is used for the transfer of patient structures and related data, between the devices found within and outside the radiotherapy department. It mainly contains the information of regions of interest (ROIs) and points of interest (E.g. dose reference points). In many cases, rather than manually drawing these ROIs on the CT images, one can indeed benefit from the automated segmentation algorithms already implemented in ITK. But at present, it is not possible to export the ROIs obtained from ITK to RTSTRUCT format. In order to bridge this gap, we have developed a framework for exporting contour data to RTSTRUCT. We provide here the complete implementation of RTSTRUCT exporter and present the details of the pipeline used. Results on a 3-D CT image of the Head and Neck (H&N) region are presented.

Exporting Contours to DICOM-RT Structure Set

Our previous contributions to the ITK community include a generalized B-spline approximation scheme as well as a generalized information-theoretic measure for assessing point-set correspondence known as the Jensen-Havrda-Charvat-Tsallis (JHCT) divergence. In this submission, we combine these two contributions for the registration of labeled point-sets. The transformation model which uses the former contribution is denoted as directly manipulated free-form deformation (DMFFD) and has been used for image registration. The information-theoretic approach described not only eliminates exact cardinality constraints which plague exact landmark matching algorithms, but it also incorporates the local point-set structure into the similarity measure calculation. Although theoretical discussion of these two components is deferred to other venues, the implementation details given in this submission should be adequate for those wishing to use our algorithm. Visualization of results is aided by another of our previous contributions. Additionally, we provide the rudimentary command line parsing classes used in our testing routines which were written in the ITK style and also available to use consistent with the open-source paradigm.

Information-Theoretic Directly Manipulated Free-Form Deformation Labeled Point-Set Registration

Fractal analysis for medical image classification and analysis was introduced in cite{Chen1989}. According to the authors, when viewed as an intensity surface, Mandelbrot\'s fractal theory provides an informative framework for characterizing such a surface. Using the fractional Brownian motion model, the authors provide an algorithm for converting a scalar image to a fractal dimension image for classification purposes or edge enhancement. This submission constitutes a report on the ITK implementation of this algorithm. 

Stochastic Fractal Dimension Image

We implement a warping method which is using the properties of elastic materials to compute the deformation between two images. The proposed elastic registration method incorporates the L2 Kantorovich-Wasserstein distance, also known as the Earth Mover’s Distance (EMD), as a similarity measure. The implemented paper presents an efficient partial differential equation approach for a first-order solution of this problem that is simpler than existing works suggested high-order solutions and is computationally simpler than existing works based on linear programming.

Specifically, we implement the following paper
Steven Haker, Lei Zhu, Allen Tannenbaum, and Sigurd Angenent, “Optimal mass transport for registration and warping”, International Journal of Computer Vision, 60(3), p. 225-240, 2004.


Optimal mass transport for deformable registration and warping of 2D images

Medical image registration is becoming a more and more useful component of a large number of applications. The presented method aims to enrich the ITK library. This method, called Morphon registration algorithm, computes a dense deformation field accepting inputs from different intensity contrasts. This article presents its implementation within the Insight Toolkit.
In this paper, we provide a brief description of the algorithm, a presentation of the implementation, the justification of our modified classes and the results given by the algorithm. We demonstrate the algorithm in application of different images intesity constrasts and dimensions.