Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a general- ized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative im- plementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading).

Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach

This document describes an ITK class implementing an Adaptive Moment Estimator (Adam) optimizer algorithm within the Insight Toolkit ITK www.itk.org. Adam is an adaptive gradient descent optimizer, which independently adaptively estimates the gradient descent step for each parameter, at each iteration, based on stored past gradients. The optimizer stores exponentially decaying averages of past gradients to estimate first moment (the mean) and the second moment (the variance) of the gradients to formulate update rule for present iteration. The Adam optimizer compares favorably to other adaptive learning-method algorithms, converges faster, and is robust to saddle point. 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. 

Adaptive Moment Estimator (Adam) Optimizer in ITK v3

Recently, the scientific community has been proposing several automatic algorithms to biomedical image segmentation procedure, being an interesting and helpful approach to assist both technicians and radiologists in this time-consuming and subjective task. One of these interesting and widely used image segmentation method could be the voxel intensity-based algorithms, e.g. image histogram threshold methods, which have been intensively improved in the past decades. Recently, an interesting approach that gained focus is the logistic classification (LC) for object detection in biomedical images. Even though the general concept behind the LC method is fairly known, the proper method's optimization still commonly adjusted by hand which naturally adds a level of uncertainty and subjectivity in the general segmentation performance. Therefore, an empirical LC optimization is presented, offering a ITK class that performs the LC parameters optimization based on empirical input data analysis. It is worth mentioning that the LogisticContrastEnhancementImageFilter class showed here is also applied on others computational problems, being briefly explained in this document.

An Empirical Optimization to Logistic Classification Model

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.

CartesianToPolar and PolarToCartesian Image Filters

A template for open science article submissions to the Insight Journal using MyST Markdown

Insight Journal MyST

We implemented the shape-based interpolation method described by Raya and Udupa in 1990 for three-dimensional images, and created two standalone filters using the Insight Toolkit ITK. The image to be interpolated must be a 3D binary image which represents an object as a series of 2D slices with pixel values at 1 inside the object and 0 outside by convention. The first filter takes as input a 3D binary image wherein the object of interest is represented in full, that is, on all contiguous slices that comprise it. Such filter yields an upsampled 3D binary image. The second filter takes as input a 3D binary image wherein the object of interest is represented only on certain slices that are regularly spaced. It yields a 3D binary image of the same size as that of the input and wherein the object is represented on all contiguous slices that comprise it. This paper is provided with the source code as well as the data used for validation.

Shape-based Interpolation of a Set of 2D Slices

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.


Voxelizer plug-in for Blender

This document describes an extension of ITK to handle both primal and dual meshes simultaneously. This paper describe in particular the data structure, an extension of itk::QuadEdgeMesh, a filter to compute and add to the the structure the dual of an existing mesh, and an adaptor which let a down- ward pipeline process the dual mesh as if it was a native itk::QuadEdgeMesh. The new data structure, itk::QuadEdgeMeshWithDual, is an extension of the already existing itk::QuadEdgeMesh, which already included by default the due topology, to handle dual geometry as well. Two types of primal meshes have been specifically illustrated: triangular / simplex meshes and Voronoi / Delaunay. A functor mechanism has been implemented to allow for different kind of computation of the dual geometry. This paper is accompanied with the source code and examples.

Primal/Dual Mesh with Application to Triangular/Simplex Mesh and Delaunay/Voronoi

Nonrigid image registration is an important, but resource demanding and time-consuming task in medical image analysis. This limits its application in time-critical clinical routines. In this report we explore acceleration of two time-consuming parts of a registration algorithm by means of parallel processing using the GPU. We built upon the OpenCL-based GPU image processing framework of the recent ITK4 release, and implemented Gaussian multi-resolution strategies and a general resampling framework. We evaluated the performance gain on two multi-core machines with NVidia GPUs, and compared to an existing ITK4 CPU implementation. A speedup factor of ~2-4 was realized for the multi-resolution strategies and a speedup factor of ~10-46 was achieved for resampling, for larger images (~10^8 voxels).

An OpenCL implementation of the Gaussian pyramid and the resampler

This document describes an ITK implementation of an interactive method for tracing curvilinear structures.
The basic tools provided in this framework are an oriented flux-based tubularity measure and a geodesic path 
tracer that uses the fast marching algorithm. The framework is efficient and requires minimal user 
interaction to trace curvilinear structures such as vessels and neurites in 2D images and 3D image 
stacks.