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Fuzzy Clustering Algorithms for Image Segmentation
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In this document we present the implementation of three fuzzy clustering algorithms using the Insight Toolkit ITK. Firstly, we developed the conventional Fuzzy C-Means that will serve as the basis for the rest of the proposed algorithms. The next algorithms [...]

Non-negative matrix factorization framework for dimensionality reduction and unsupervised clustering
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Non-negative Matrix Factorization (NMF) is a robust approach to learning spatially localized parts-based subspace patterns in applications such as document analysis, image interpretation, and gene expression analysis. NMF-based decomposition capabilities are [...]

Left Ventricle Segmentation in Cardiac Ultrasound Using Hough-Forests With Implicit Shape and Appearance Priors
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We propose a learning based approach to perform automatic segmentation of the left ventricle in 3D cardiac ultrasound images. The segmentation contour is estimated through the use of a variant of Hough forests whose object localization capabilities are [...]

Endocardial Segmentation using Structured Random Forests in 3D Echocardiography
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Segmentation of the left ventricle endocardium in 3D echocardiography is a critical step for the diagnosis of heart disease. Although recent work has shown effective endocardial edge detection, these techniques still preserve spurious anatomical edge [...]

Pseudo-CT generation from multiple MR images for small animal irradiation
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Introduction. Computed tomography (CT) is the standard imaging modality for radiation therapy treatment planning (RTTP) because of its ability to provide information on electron density. However, magnetic resonance (MR) imaging provides superior soft tissue [...]

3D Segmentation in the Clinic: A Grand  Challenge II: MS lesion segmentation
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This paper describes the setup of a segmentation competition for the automatic extraction of Multiple Sclerosis (MS) lesions from brain Magnetic Resonance Imaging (MRI) data. This competition is one of three competitions that make up a comparison workshop at [...]

Endocardial 3D Ultrasound Segmentation using Autocontext Random Forests
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In this paper, we present the use of a generic image segmentation method, namely a succession of Random Forest classifiers in an autocontext framework, for the MICCAI 2014 Challenge on Endocardial 3D Ultrasound Segmentation (CETUS). The proposed method [...]

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 [...]

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 [...]

Challenge on Endocardial Three-dimensional Ultrasound Segmentation (CETUS)
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Real-time 3D echocardiography has already been shown to be an accurate tool for left ventricular (LV) volume assessment. However, LV border identification remains a challenging task, mainly because of the low contrast of the images combined with drop-out [...]

Minimally Interactive Knowledge-based Coronary Tracking in CTA using a Minimal Cost Path
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An algorithm for minimally interactive coronary artery tracking is presented. Tracking ability and accuracy results are demonstrated on 16 images CTA images. First, a region of interest is automatically selected and a denoising filter applied. Then, for each [...]


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