Characterization of anatomical structure of the brain and efficient algorithms for automatically analyzing brain MRI have gained an increasing interest in recent years. In this paper, we propose an algorithm that automatically segments the anatomical structures of magnetic resonance human brain images. Our method uses the prior knowledge of labels given by experts to statistically investigate the spatial correspondences of brain structures in subject images. We create a multi-atlas by registering the training images to the subject image and then propagating corresponding labels to the graph of the image. Label fusion then combines these multiple labels of atlases into one label at each voxel with intensity similarity based weighted voting. Finally we cluster the graph using multiway cut in order to achieve the final 3D segmentation of the subject image. The promising evaluation results of our segmentation method on the MRBrainS13 Test Dataset shows the efficiency and robustness of our algorithm.

Multi-Atlas Brain MRI Segmentation with Multiway Cut

We present a new filter for the Insight Toolkit (ITK) for reducing the resolution of an image by an integer factor while averaging called BinShrink. This filter provides a new level of performance to ITK for reducing resolution and noise present in an image. The filter supports streaming, multi-threading and most of ITK's pixel types including scalars, Vectors, SymmetricSecondRankTensors, and RGBPixels. The filter has been optimized to efficiently access the input image thereby greatly increasing performance over conventional methods.

BinShrink: A multi-resolution filter with cache efficient averaging.

This document describes the implementation of an ITK class to support the reading and writing of Meshes in STL file format. The Meshes are assumed to contain 2D manifolds embedded in a 3D space. In practice, it would be desirable to use this class mostly to read and write QuadEdgeMeshes.

This paper is accompanied with the source code, input data, parameters and output data that the authors 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.

STL file format MeshIO class for ITK

This article describes implementation of a CLI module to merge an image into the space of another adjacent image using the Insight Toolkit. The algorithm constructs a smoothly stitched image out of two or more smaller images. The overlapping regions of the images are used to align the images. The registration method is derived from ITK's Registration framework. The algorithm works with 2D and 3D images.

Using TubeTK to Automatically Merge Volumes

Several applications such as multi-atlas segmentation require frequent iteration over multiple image volumes at the same time.
Doing so with the regular ITK iterators is tedious and error prone as it requires updating each iterator at end of each iteration. Failing to do so results in hard to debug errors and crashes.
The MultipleImageIterator is a simple wrapper class that tries to make this more convenient.


A MultipleImageIterator for iterating over multiple images simultaneously

Three-dimensional laser scanner is a modern technology used to detect and capture the shape of complex objects. There are a lot of possible application fields for this technology: applications in the industrial sector (reverse engineering), in the medical field or in the cultural heritage field for virtual reconstruction of works of art. 
The main goal of this work is to make accessible the three-dimensional models obtained from laser scans on mobile devices (smartphones, tablets), exploiting a remote rendering application on high-performance workstations.
This paper also shows how this can be done fairly easily using one of the most popular instruments in the field of scientific visualization, VTK.

VTK Remote Rendering of 3D Laser Scanner Ply files for Android Mobile Devices

In this article, we present an implementation of a flexible framework for non-parametric variational image registration, realized as part of ITK's finite difference solver hierarchy.

In a variational registration setting, the transformation is found by minimizing an energy functional that consists of (at least) two terms: a distance measure between fixed and transformed moving image and a smoothness condition for the transformation. The specific choice of these terms depends on the particular application and requires consideration of, for example, image content and imaging modalities. Following this view, the presented framework can be seen as a generalization of the demons algorithm in which two key aspects remain exchangeable: the force term (or registration function, according to the distance measure) and the regularizer (according to the smoothness condition). Moreover, two transformation models are realized: either a dense displacement field or a stationary velocity field to restrain the transformation to the space of diffeomorphisms.

In its current state, the framework includes implementations of forces based on the Sum of Squared Differences (SSD), Normalized Cross Correlation (NCC) and the demons algorithm as well as Gaussian, diffusion and elastic smoothing. However, the implementation of further components is possible and encouraged.


A Flexible Variational Registration Framework

This talk presents the Open-Source toolkits developed by Kitware for big data analysis.

Big Data Analysis and Visualization with Open Source Tools

Personalized simulations can help assess and predict the clinical status of patients and are a key tenet of the Virtual Physiological Human (VPH) framework. This paper shows how the turnkey access of GIMIAS, an open source software platform built on open source toolkits (VTK, MITK,ITK), thanks to his inclusion in VPH-Share, an infrastructure European project with open-source components, was key to progressing the deployment effectiveness for MySpine, a VPH European project, among his geographically dispersed community

Leveraging Open Source for Geographically Dispersed Workflows

In order to guaranty performances of complex systems using numerical simulation, CEA is performing advanced data analysis and scientific visualization with open source software using High Performance Computing (HPC) capability. The diversity of the physics to study produces results of growing complexity in terms of large-scale, high dimensional and multivariate data. Moreover, the HPC approach introduces another layer of complexity by allowing computation amongst thousands of remote cores accessed from sites located hundreds of kilometers away from the computing facility.
This paper presents how CEA deploys and contributes to open source software to enable production class visualization tools in a high performance computing context. Among several open source projects used at CEA, this presentation will focus on Visit, VTK and Paraview.
In the first part we will address specific issues encountered when deploying VisIt and Paraview in a multi-site supercomputing facility for end-users. Several examples will be given on how such tools can be adapted to take advantage of a parallel setting to explore large multi-block dataset or perform remote visualization on material interface reconstructions of billions of cells. Then, the specific challenges faced to deliver Paraview’s Catalyst capabilities to end-users will be discussed.
In the second part, we will describe how CEA contributes to open source visualization software and associated software development strategy by emphasizing on two recent development projects. The first is an integrated simulation workbench providing plugins for every step required to achieve numerical simulation independently on a local or a remote computer. Embedded in an Eclipse RCP environment, VTK views allow the users to perform data input using interaction or mesh preview before running the simulation code. Contributions to VTK have been made in order to smoothly integrate these technologies. The second details how recent developments at CEA have helped to visualize and to analyze results from ExaStamp, a parallel molecular dynamics simulation code dealing with molecular systems ranging from a few millions up to a billion atoms. These developments include a GPU intensive rendering method specialized for atoms and specific parallel algorithms to process molecular data sets.