scholarly journals An image sampling framework for the ITK

2010 ◽  
Author(s):  
Marius Staring ◽  
Stefan Klein
Keyword(s):  
Image Registration ◽  
Similarity Measure ◽  
Input Data ◽  
Source Code ◽  
Input Image ◽  
Output Data ◽  
Image Intensity ◽  
Image Sampling ◽  
A Value ◽  
Voxel Grid

This document describes the implementation of image samplers using the Insight Toolkit ITK url{www.itk.org}. Image samplers take a set of `picks’ from an image and store them in an array. A sample consists of the location of the pick (a point) and the corresponding image intensity (a value). Image samplers are useful for image registration, where samples are drawn from the fixed image in order to compute the similarity measure. Together with an image sampler base class, we introduce the following image samplers: 1) a full sampler that draws all voxel coordinates from the input image, 2) a grid sampler that draws samples from a user-specified regular voxel grid, 3 and 4) two random samplers that uniformly draw a user-specified number of samples from the input image.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.

scholarly journals N-Dimensional Computation of Strain Tensor Images in the Insight Toolkit

2017 ◽  
Author(s):  
Matthew Mccormick
Keyword(s):  
Image Registration ◽  
Constitutive Model ◽  
Displacement Field ◽  
Input Data ◽  
Source Code ◽  
Strain Tensor ◽  
Fundamental Principle ◽  
Local Deformation ◽  
Output Data ◽  
Scientific Publications

Strain quantifies local deformation of a solid body. In medical imaging, strain reflects how tissue deforms under load. Or, it can quantify growth or atrophy of tissue, such as the growth of a tumor. Additionally, strain from the transformation that results from image-to-image registration can be applied as an input to a biomechanical constitutive model.This document describes N-dimensional computation of strain tensor images in the Insight Toolkit (ITK), www.itk.org. Two filters are described. The first filter computes a strain tensor image from a displacement field image. The second filter computes a strain tensor image from a general spatial transform. In both cases, infinitesimal, Green-Lagrangian, or Eulerian-Almansi strain can be generated.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.

scholarly journals A GPU-based Implementation of Multimodal Deformable Image Registration Based on Mutual Information or Bhattacharyya Distance

2011 ◽  
Author(s):  
Yifei Lou ◽  
Xun Jia ◽  
Xuejun Gu ◽  
Allen Tannenbaum
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Input Data ◽  
Fluid Model ◽  
Source Code ◽  
Deformable Image Registration ◽  
Bhattacharyya Distance ◽  
Output Data ◽  
Registration Method ◽  
Viscous Fluid Model

This paper describes a multimodal deformable image registration method on the GPU. It is a CUDA-based implementation of a paper by E. D’Agostino et. al, ‘’A viscous fluid model for multimodal non-rigid image registration using mutual information’’. In addition, we incorporate an alternative metric as opposed to mutual information, called Bhattacharyya Distance, in the recent work of Lou and Tannenbaum. This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm.

scholarly journals Introduction to ITK Resample In-Place Image Filter

2010 ◽  
Author(s):  
Wei Lu ◽  
Hans Johnson
Keyword(s):  
Input Data ◽  
Source Code ◽  
Physical Space ◽  
Fundamental Principle ◽  
Input Image ◽  
Space Representation ◽  
Output Data ◽  
Scientific Publications ◽  
Image Filter ◽  
Place Image

In this document we present an ITK image filter that outputs a new image with identical voxel contents but with modified physical space representation. The physical space representation of an image is modified to reflect the physical space transform on the input image. The advantage of this filter is that it is free from interpolation error. This paper is accompanied with the source code, input data, parameters and output data that the au- thors 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.

scholarly journals Experimenting with Segmentation in ITK

2007 ◽  
Author(s):  
Cemal Cagatay Bilgin
Keyword(s):  
Open Problem ◽  
Input Data ◽  
Source Code ◽  
Output Data

This document describes some experiments for segmentation using the Insight Toolkit ITK . The code of the experiment is written following the ITK CodingStyle as described in the directory .Segmentation is still an open problem and there are quite a lot of algorithms specific to application domains and data being used. In this experiment I’ve used a 3-D MRA scan of Brain retrieved from MIDAS website. This paper is accompanied with the source code, input data, parameters and output data that the author used for experimenting with the algorithm.

scholarly journals Voxelizer plug-in for Blender

2012 ◽  
Author(s):  
Roman Grothausmann ◽  
Julien Gout ◽  
Mark Kühnel
Keyword(s):  
Input Data ◽  
Source Code ◽  
Output Data ◽  
Vector Graphics ◽  
3D Mesh ◽  
Voxel Data

This document describes a plug-in for Blender (www.blender.org) that allows to rasterize 3D mesh objects into 3D voxel data, i. e. it allows to voxelize Blender meshes. In 2D, this process can be compared to rasterization of vector graphics into pixel graphics. The voxelization is done by VTK (www.vtk.org) functions. A simple GUI allows to choose the type of voxelization and to specify necessary parameters. Depending on the type chosen, only the surface of the mesh objects is voxelized or the enclosed volume, i. e.“filled”. Beside the size of the output, one can specify if the result should be “anti-aliased” or not. This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the script described in this paper.

scholarly journals Iterative Smoothing of Field Data in Spherical Meshes

2010 ◽  
Author(s):  
Luis Ibanez ◽  
B.t. thomas Yeo ◽  
Polina Golland
Keyword(s):  
Vector Field ◽  
Scalar Field ◽  
Input Data ◽  
Field Data ◽  
Source Code ◽  
Fundamental Principle ◽  
Mesh Smoothing ◽  
Output Data ◽  
Scientific Publications

This document describes a contribution to the Insight Toolkit intended to smooth the values of Field data associated with the nodes of a Spherical Mesh. The Mesh Smoothing filters contributed here do not modify the geometry or the topology of the Mesh. They act only upon the pixel data values associated with the nodes. Two filters are presented, one that smooths scalar field data, and a second one that smooths vector field data. 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.

scholarly journals Spherical Thin Plate Spline Mesh Filter

2012 ◽  
Author(s):  
Wen Li ◽  
Vincent Magnotta
Keyword(s):  
Thin Plate ◽  
Input Data ◽  
Source Code ◽  
Spherical Geometry ◽  
Fundamental Principle ◽  
Thin Plate Spline ◽  
Output Data ◽  
Scientific Publications ◽  
And Topology

This document describes a contribution to the Insight Toolkit intended to perform landmark-based registration on two meshes. The method implemented here is restricted to meshes with a spherical geometry and topology. Please refer Wahba’s paper for the mathematical details and Zou’s paper for the applications. 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.

scholarly journals STL file format MeshIO class for ITK

2014 ◽  
Author(s):  
Luis Ibanez
Keyword(s):  
Input Data ◽  
Source Code ◽  
Fundamental Principle ◽  
File Format ◽  
Output Data ◽  
Scientific Publications ◽  
Reading And Writing ◽  
Stl File ◽  
3D Space

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.

scholarly journals Image Segmentation: Ventricle Isolation Using Region Growing

2007 ◽  
Author(s):  
Julio Lajara
Keyword(s):  
Image Segmentation ◽  
Input Data ◽  
Source Code ◽  
Region Growing ◽  
Scientific Publication ◽  
Input File ◽  
Output Data ◽  
Data Subject

This document describes the process used in implementing image segmentation through a region growing algorithm with the use of the Insight Toolkit (ITK) imaging program. The code used to implement the region growing algorithm is documented in the ITK Software Guide and makes use of the ITK library. This paper is accompanied with all source code, input data, parameters and output data that were originally used in developing the image segmentation application, except where prior laws or agreements prohibit redistribution of such material. The only data subject to not being redistributable was the input file used for testing which was a brain dataset from Kitware’s MIDAS collection named Normal090-T2.mha. The rest of this scientific publication is written in a manner such that it facilitates reproducibility of the reported results.

scholarly journals CartesianToPolar and PolarToCartesian Image Filters

2019 ◽  
Author(s):  
Bhavya Ajani ◽  
Sikander Sharda
Keyword(s):  
Image Processing ◽  
Input Data ◽  
Source Code ◽  
Fundamental Principle ◽  
Polar Space ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Output Data ◽  
Scientific Publications ◽  
Image Filters

In this paper, we describe a set of filters, implemented in the Insight Toolkit www.itk.org, for converting an image from Cartesian co-ordinate space to Polar co-ordinate space and vice-versa. Cartesian to Polar conversion of an image is a useful operation in preprocessing stage of certain image-processing algorithm where feature of interest has simplified representation in the polar space. 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.

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