This document presents an implementation of a stratified mesh sampling algorithm in the VTK framework. This code was adapted directly from the original implementation by Diego Nehab, with permission. We present a class, vtkStratifiedSampling, which uniformly samples a mesh. A Paraview plugin interface is provided to allow extremely easy experimentation with the new functionality. We propose these classes as an addition to the Visualization Toolkit.

Stratified Mesh Sampling for VTK

Segmenting images into "super pixels" is lately a very hot topic. One implementation of such a technique is by Felzenszwalb and Huttenlocher. We propose a new class, vtkSuperPixelSegmentation, which has wrapped Felzenszwalb's code (with permission) to provide this functionality in the VTK framework. The latest version includes an ITK filter as well as the original VTK filter. A GUI is also now available to interactively set the filter parameters and see the resulting segments. The code is available here: https://github.com/daviddoria/SuperPixelSegmentation.

Graph Cuts Based Super Pixel Segmentation for VTK

Several kind of noise can be found in real images, mostly depending on the modality
of acquisition. It is often useful to be able to simulate that noise, for example
to test the behavior of an algorithm in the presence of a known amount of noise.

This contribution provides the filters to generate four kind of noise -- additive
gaussian, shot, speckle and salt and pepper -- as well as a PSNR calculator.


Noise simulation

This document describes an open source, high performance ultrasound B-Mode implementation based on the Insight Toolkit (ITK). ITK extensions are presented to calculate the radio-frequency (RF) signal envelope. A variety of 1D Fast Fourier Transform options are introduced including VNL, FFTW, and an OpenCL solution. Scan conversion is implemented for phased array or curvilinear transducers. The entire image processing pipeline is streamable to limit memory consumption during multi-frame or 3D acquisitions with the introduction of an itk::StreamingResampleImageFilter.


An Open Source, Fast Ultrasound B-Mode Implementation for Commodity Hardware

Segmentation forms the onset for image analysis especially for medical images, making any abnormalities in tissues distinctly visible. Possible application includes the detection of tumor boundary in SPECT, MRI or electron MRI (EMRI). Nevertheless, tumors being heterogeneous pose a great problem when automatic segmentation is attempted to accurately detect the region of interest (ROI). Consequently, it is a challenging task to design an automatic segmentation algorithm without the incorporation of 'a priori' knowledge of an organ being imaged. To meet this challenge, here we propose an intelligence-based approach integrating evolutionary k-means algorithm within multi-resolution framework for feature segmentation with higher accuracy and lower user interaction cost. The approach provides several advantages. First, spherical coordinate transform (SCT) is applied on original RGB data for the identification of variegated coloring as well as for significant computational overhead reduction. Second the translation invariant property of the discrete wavelet frames (DWF) is exploited to define the features, color and texture using chromaticity of LL band and luminance of LH and HL band respectively. Finally, the genetic algorithm based K-means (GKA), which has the ability to learn intelligently the distribution of different tissue types without any prior knowledge, is adopted to cluster the feature space with optimized cluster centers. 
Experimental results of proposed algorithm using multi-modality images such as MRI, SPECT, and EMRI are presented and analyzed in terms of error measures to verify its effectiveness and feasibility for medical applications.

An Intelligent cluster-based segmentation using Wavelet Features:Application to Medical Images

Region growing is a technique that can be used to propagate information over a mesh. We provide an iterator that can be used with vtkPointSet subclasses to traverse a mesh in a reasonable fashion. A "front" is emanated from a selected starting vertex, and the iterator provides the ID of the next vertex on this front.

A Mesh Front Iterator for VTK

Region growing is a technique that can be used to propagate information over a mesh. In a previous submission, "A Mesh Front Iterator for VTK", we introduced an iterator that can be used with vtkPointSet subclasses to traverse a mesh. It is sometimes useful to visit only vertices that are "similar" to their neighbors by some definition. This concept can be used to select many types of common regions, including planar regions or regions with similar color, to name a few. In this paper, we propose an iterator which propagates on a mesh using a condition test which can be easily modified.

The latest code is available here: https://github.com/daviddoria/MeshConditionalFrontIterator

A Conditional Mesh Front Iterator for VTK

This document describes an implementation of a filter to perform Boolean operations between surfaces. The filter requires the GNU Triangulated Surface Library GTS to perform the operations. This document is accompanied with the source code, and the xml files necessary to create a paraview plugin.

Boolean Operations on Surfaces for VTK

The image registration framework in the Insight Tookit offers a powerful body of code for finding the optimal spatial transform that registers one image with another. However, ITK currently lacks a way to fit parametric models of image pixel values to an input image. This document describes new classes that enable use of the registration framework to provide this capability. We describe a new base class, itk::ParametricImageSource, that defines an interface for parametric image sources. An adapter class itk::ImageToParametricImageSourceMetric that enables itk::ParametricImageSources to be hooked into the registration framework is also described. An example adapter class that enables the existing itk::GaussianImageSource to be used for image fitting is presented, and we demonstrate use of the classes by fitting a 2D Gaussian function to an image generated by the itk::GaussianImageSource class.

Adapting the ITK Registration Framework to Fit Parametric Image Models

Tiff images are widely used and domain and instrument specific
information is often included in some tags. There isn't any standard
way of making use of this information in ITK at present. This article
introduces some minor additions to the ITK tiff reader to include the
content of some potentially useful tags in the image
MetaDataDictionary, thus enabling domain specific information to be
extracted from the tags within an ITK application.
