A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model.
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TLDR
A segmentation tool is presented in order to differentiate the anatomical structures within the vectorial volume of the CT uroscan to get a better classification result and is less affected by the noise.About:
This article is published in Computerized Medical Imaging and Graphics.The article was published on 2009-12-01 and is currently open access. It has received 26 citations till now. The article focuses on the topics: Scale-space segmentation & Mixture model.read more
Citations
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Journal ArticleDOI
Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review
Maria Eugenia Caligiuri,Paolo Perrotta,Antonio Augimeri,Federico Rocca,Aldo Quattrone,Andrea Cherubini +5 more
TL;DR: It is concluded that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives.
Journal ArticleDOI
Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images.
Rita Lopes Simoes,Christoph Mönninghoff,Martha Dlugaj,Christian Weimar,Isabel Wanke,Anne-Marie van Cappellen van Walsum,Anne-Marie van Cappellen van Walsum,Cornelis H. Slump +7 more
TL;DR: This work proposes an automatic lesion segmentation method that uses only three-dimensional fluid-attenuation inversion recovery (FLAIR) images and uses a modified context-sensitive Gaussian mixture model to determine voxel class probabilities, followed by correction of FLAIR artifacts.
Journal ArticleDOI
Image segmentation using spectral clustering of Gaussian mixture models
TL;DR: A novel image segmentation method that combines spectral clustering and Gaussian mixture models is presented in this paper and the experimental evaluation on the IRIS dataset and the real-world image segmentsation problem demonstrates the effectiveness of the proposed approach.
Journal ArticleDOI
Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods
TL;DR: An accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the corpus callosum was developed and can be adapted to perform segmentation on other regions of the brain.
Journal ArticleDOI
Accurate image segmentation using Gaussian mixture model with saliency map
TL;DR: This paper proposes a new model, which incorporates the image content-based spatial information extracted from saliency map into the conventional GMM, and shows that the proposed method outperforms the state-of-the-art methods in terms of accuracy and computational time.
References
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Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms.
TL;DR: Overall, the newly proposed AFCM segmentation technique is recommended in MRI segmentation, which has better detection of abnormal tissues than FCM according to a window selection.
Proceedings ArticleDOI
Spatialized transfer functions
TL;DR: This work presents an automatic yet powerful method for the automatic setup of multi-dimensional transfer functions by adding spatial information to the histogram of a volume by an extension of the wellknown pre-integration technique.
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Enhanced Spatial Priors for Segmentation of Magnetic Resonance Imagery
TL;DR: A Bayesian, model-based method for segmentation of Magnetic Resonance images is proposed, and a discrete vector valued Markov Random Field model is used as a regularizing prior in a Bayesian classification algorithm to minimize the effect of salt-and-pepper noise common in clinical scans.
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Statistical intensity correction and segmentation of MRI data
TL;DR: A statistical method that uses knowledge of tissue properties and intensity inhomogeneities to correct for these intensity inhmogeneities in MR images is described, and is fully automatic for segmenting healthy brain tissue.
Proceedings ArticleDOI
Extending and simplifying transfer function design in medical volume rendering using local histograms
TL;DR: This paper introduces Partial Range Histograms in an automatic tissue detection scheme, which in connection with Adaptive Trapezoids enable efficient TF design and proposes a fuzzy classification based on local histograms as a second TF dimension.