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This brief document provides an introduction to an external ITK module, DVMeshNoise. This module provides classes for perturbing the positions of the vertices of either an itk::Mesh or itk::QuadEdgeMesh with Gaussian noise. This may be useful in testing the robustness of an algorithm to small changes in the input mesh, augmenting datasets for machine learning, and counteracting deleterious effects which highly regular regions of a mesh may occasionally have on mesh processing. After demonstrating basic usage, we present a practical example to show how these classes can be incorporated into a pipeline to improve mesh quality.

Perturbing Mesh Vertices with Additive Gaussian Noise

In an earlier Insight Journal article, we introduced an ITK implementation of the adaptive patch-based image denoising algorithm described in [3]. We follow-up up that offering with a generalized non-local, patch-based ITK class framework and a refactored denoising class. In addition, we provide two ITK implementations of related, well-known algorithms. The first is a non-local super resolution method described in [1, 2]. The second is the multivariate joint label fusion algorithm of [4, 5] with additional extensions, denoted as “joint intensity fusion”, which will be described in a forthcoming manuscript. Accompanying these ITK classes are documented programming interfaces which use our previously introduced unique command line interface routines. Several 2-D examples on brain imaging data are provided to qualitatively demonstrate performance.

A patch-based framework for new ITK functionality: Joint fusion, denoising, and non-local super-resolution

The anisotropic diffusion algorithm has been intensively studied in the past decades, which could be considered as a very efficient image denoising procedure in many biomedical applications. Several authors contributed many clever solutions for diffusion parameters fitting in specific imaging modalities. Furthermore, besides improvements regarding the image denoising quality, one important variable that must be carefully set is the conductance, which regulates the structural edges preservation among the objects presented in the image. The conductance value is strongly dependent on image noise level and an appropriate parameter setting is, usually, difficult to find for different images databases and modalities. Fortunately, thanks to many efforts from the scientific community, a few automatic methods have been proposed in order to set the conductance value automatically. Here, it is presented an ITK class which offers a simple collection of the most common automatic conductance setting approaches in order to assist researchers in image denoising procedures using anisotropic-based filtering methods (such as well described in the AnisotropicDiffusionFunction class).

Automatic Conductance Estimation Methods for Anisotropic Diffusion ITK Filters

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.

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

This document describes a new remote module implemented for the Insight Toolkit ITK, itkTextureFeatures. This module contains two texture analysis filters that are used to compute feature maps of N-Dimensional images using two well-known texture analysis methods. The two filters contained in this module are itkScalarImageToTextureFeaturesImageFilter (which computes textural features based on intensity-based co-occurrence matrices in the image) and itkScalarImageToRunLengthFeaturesImageFilter (which computes textural features based on equally valued intensity clusters of different sizes or run lengths in the image). The output of this module is a vector image of the same size than the input that contains a multidimensional vector in each pixel/voxel. Filters can be configured based in the locality of the textural features (neighborhood size), offset directions for co-ocurrence and run length computation, the number of bins for the intensity histograms, the intensity range or the range of run lengths. This paper is accompanied with the source code, input data, parameters and output data that we have 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.

Computing Textural Feature Maps for N-Dimensional images

This document describes the implementation of the external module ITKIsotropicWavelets, a multiresolution (MRA) analysis framework using isotropic and steerable wavelets in the frequency domain. This framework provides the backbone for state of the art filters for denoising, feature detection or phase analysis in N-dimensions. It focus on reusability, and highly decoupled modules for easy extension and implementation of new filters, and it contains a filter for multiresolution phase analysis,

The backbone of the multi-scale analysis is provided by an isotropic band-limited wavelet pyramid, and the detection of directional features is provided by coupling the pyramid with a generalized Riesz transform.
The generalized Riesz transform of order N behaves like a smoothed version of the Nth order derivatives of the signal. Also, it is steerable: its components impulse responses can be rotated to any spatial orientation, reducing computation time when detecting directional features.

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


Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework.

The Insight Toolkit (ITK) utilizes a generic design for image processing filters that allows many developers to rapidly implement new algorithms. While ITK filters benefit from a platform-independent and versatile multithreading capability, the current implementation does not easily achieve high performance. First, ITK relies on a static decomposition of the image into subsets of equal size which is highly inefficient when the computational complexity varies between subsets (unbalanced workloads). Second, the current domain decomposition is limited to subdivide the input domain along a single dimension (typically the slice dimension in a 3-D volume), which causes a multithreading under-utilization when the number of threads is larger than the size of this dimension when using massively parallel compute systems. We previously presented a new itk::TBBImageToImageFilter class that replaced the static task decomposition by a dynamic task decomposition for improved workload balancing, in which the job scheduling task was optimized using the Intel® Threading Building Blocks (TBB) library. In this work, we propose a new multidimensional dynamic image decomposition approach that allows decomposition over an arbitrary number of dimensions. This new generic multithreading capability, combined with the TBB dynamic task scheduler, substantially improves multithreading performance when using massively parallel processors.

Efficient multithreading for manycore processor: Multidimensional domain decomposition using Intel® TBB

This document describes a new remote module implemented for the Insight Toolkit (ITK), itkBoneMorphometry. This module contains bone analysis filters that compute features from N-dimensional images that represent the internal architecture of bone. The computation of the bone morphometry features in this module is based on well known methods. The two filters contained in this module are itkBoneMorphometryFeaturesFilter. which computes a set of features that describe the whole input image in the form of a feature vector, and itkBoneMorphometryFeaturesImageFilter, which computes an N-D feature map that locally describes the input image (i.e. for every voxel). itkBoneMorphometryFeaturesImageFilter can be configured based in the locality of the desired morphometry features by specifying the neighborhood size. This paper is accompanied by the source code, the input data, the choice of parameters and the output data that we have used for validating the algorithms described. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.