Information-Theoretic Directly Manipulated Free-Form Deformation Labeled Point-Set Registration
Our previous contributions to the ITK community include a generalized B-spline approximation scheme as well as a generalized information-theoretic measure for assessing point-set correspondence known as the Jensen-Havrda-Charvat-Tsallis (JHCT) divergence. In [...]

Boolean Operations on Surfaces 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 [...]

Level Set Segmentation: Active Contours without edge
An Insight Toolkit (ITK) processing framework for segmentation using active contours without edges is presented in this paper. Our algorithm is based on the work of Chan and Vese [1] that uses level- sets to accomplish region segmentation in images with [...]

Image Segmentation Project - Open source software practices class
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This document describes implementation of two Level-set segmentation algorithms using Insight Toolkit ITK . The algorithms chosen for implementation are are 1) Geodesic Active contour Levelset segmentation and 2) Shape Detection Levelset segmentation. The [...]

level set segmentation using coupled active surfaces
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An Insight Toolkit (ITK) processing framework for simultaneous segmentation of multiple objects using active contours without edges is presented in this paper. These techniques are also popularly referred to as multiphase methods. Earlier, we had an [...]

A Novel Information-Theoretic Point-Set Measure Based on the Jensen-Havrda-Charvat-Tsallis Divergence
A novel point-set registration algorithm was proposed in [6] based on minimization of the Jensen-Shannon divergence. In this contribution, we generalize this Jensen-Shannon divergence point-set measure framework to the Jensen-Havrda-Charvat-Tsallis [...]

Fully automatic brain segmentation using model-guided level sets and skeleton-based models
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A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted [...]

Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification
In this paper a specific method is presented to facilitate the semi-automatic segmentation of liver metastases in CT images. Accurate and reliable segmentation of tumors is e.g. essential for the follow-up of cancer treatment. The core of the algorithm is a [...]

A Minimal Cost Path and Level Set Evolution Approach for Carotid Bifurcation Segmentation
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We propose a new approach for semi-automatic segmentation of the carotid bifurcation as part of the Carotid Lumen Segmentation and Stenosis Grading Challenge MICCAI'2009 workshop. Three initial points are provided as input, belonging to the Common, the [...]

Segmentation of Head and Neck CT Scans Using Atlas-based Level Set Method
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In this paper, we present an atlas-based level set automatic method for segmenting anatomical structures in head and neck CT data, such as mandible and brainstem. The proposed method is a hybrid method that combines two aspects. First, we register the atlas [...]

Point Set Surface Reconstruction for VTK
This document presents a set of classes (vtkPointSetSurfaceReconstruction, vtkVoxelizePolyData) to produce a surface from an oriented point set. These classes are implemented as VTK filters. A Paraview plugin interface is provided to allow extremely easy [...]

Point Set Processing for VTK - Outlier Removal, Curvature Estimation, Normal Estimation, Normal Orientation
This document presents a set of classes (vtkPointSetOutlierRemoval, vtkPointSetNormalEstimation, vtkPointSetNormalOrientation, vtkPointSetCurvatureEstimation, vtkEuclideanMinimumSpanningTree, and vtkRiemannianGraphFilter) to enable several basic operations on [...]

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ISSN 2328-3459
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