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Automated Walks using Machine Learning for Segmentation
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This paper describes an automated algorithm for segmentation of brain structures (CSF, white matter, and gray matter) in MR images. We employ machine learning, i.e. k-Nearest Neighbors, of features derived from k-means, Canny edge detection, and Tourist [...]

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 [...]

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 [...]

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 [...]

Brain Tumor Progression Modeling - A Data Driven Approach
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Malignant gliomas are highly heterogeneous brain tumors with complex an- isotropic growth patterns and occult invasion. Computational modeling of cell migration and proliferation has been subject of intensive research aiming at a deeper understanding of the [...]

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 [...]

Multimodal Analysis of Vasogenic Edema in Glioblastoma Patients for Radiotherapy Planning
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Glioblastoma (GBM) is the most common type of primary brain tumor, which is characterized by an infiltrative growth pattern. In current practice, radiotherapy planning is primarily based upon T2 FLAIR MRI despite its known lack of specificity in the detection [...]

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 [...]

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 [...]

Probabilistic Tissue Characterization for Ultrasound Images
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This document describes the derivation of the mixture models commonly used in the literature to describe the probabilistic nature of speckle: The Gaussian Mixture Model, the Rayleigh Mixture Model, the Gamma Mixture Model and the Generalized Gamma Mixture [...]

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 [...]

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 [...]

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 [...]

An entropy based multi-thresholding method for semi-automatic segmentation of liver tumors
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Liver cancer is the fifth most commonly diagnosed cancer and the third most common cause of death from cancer worldwide. A precise analysis of the lesions would help in the staging of the tumor and in the evaluation of the possible applicable therapies. In [...]


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