The VTK Journal is an Open Access on-line publication covering the domain of scientific visualization.

The unique characteristics of the VTK Journal include:

-Open-access to articles and reviews
-Open peer-review that invites discussion between reviewers and authors
-Support for continuous revision of articles, code, and reviews

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Multi-Atlas Brain MRI Segmentation with Multiway Cut
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Characterization of anatomical structure of the brain and effi cient algorithms for automatically analyzing brain MRI have gained an increasing interest in recent years. In this paper, we propose an algorithm that automatically segments the anatomical [...]

Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)
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In this paper, we validate our proposed segmentation algorithm called Bayesian-based adaptive mean-shift (BAMS) on real mul-timodal MR images provided by the MRBrainS challenge. BAMS is a fully automatic unsupervised segmentation algorithm. It is based on the [...]

Modified Expectation Maximization Method for Automatic Segmentation of MR Brain Images
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An automated method of MR Brain image segmentation is presented. A block based Expectation Maximization method is presented for the tissue classification of MR Brain images. The standard Gaussian Mixture Model is the most widely used method for MR Brain Image [...]

Automatic Brain Tissue Segmentation of Multi-sequence MR Images Using Random Decision Forests
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This work is integrated in the MICCAI Grand Challenge: MR Brain Image Segmentation 2013. It aims for the automatic segmentation of brain into Cerebrospinal fluid (CSF), Gray matter (GM) and White matter (WM). The provided dataset contains patients with white [...]

Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification
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We present a method for automated brain-tissue segmentation through voxelwise classification. Our algorithm uses manually labeled training images to train a support vector machine (SVM) classifier, which is then used for the segmentation of target images. The [...]

Gaussian Intensity Model with Neighborhood Cues for Fluid-Tissue Categorization of Multi-Sequence MR Brain Images
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This work presents an automatic brain MRI segmentation method which can classify brain voxels into one of three main tissue types: gray matter (GM), white matter (WM) and Cerebro-spinal Fluid (CSF). Intensity-model based classification of MR images has proven [...]

MR Brain Segmentation using Decision Trees
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Segmentation of the human cerebrum from magnetic resonance images (MRI) into its component tissues has been a defining problem in medical imaging. Until recently, this has been solved as the tissue classification of the T1-weighted (T1-w) MRI, with numerous [...]

MAP–Based Framework for Segmentation of MR Brain Images Based on Visual Appearance and Prior Shape
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We propose a new MAP-based technique for the unsupervised segmentation of different brain structures (white matter, gray matter, etc.) from T1-weighted MR brain images. In this paper, we follow a procedure like most conventional approaches, in which [...]

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 [...]

Multi-Atlas-based Segmentation with Hierarchical Max-Flow
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This study investigates a method for brain tissue segmentation from 3D T1 weighted (T1w) MR images via convex relaxation with a hierarchical ordering constraint. It employs a multi-atlas-based initialization from 5 training images and is tested on 12 T1w MR [...]

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Publication of the Month
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Boolean Operations on Surfaces in VTK Without External Libraries
by Quammen C., Weigle C., Taylor II R.M.

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ISSN 2328-3459
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