A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model.
Reads0
Chats0
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
More filters
Book ChapterDOI
Multiple Frame CT Image Sequencing Big Data Batch Clustering Method.
TL;DR: This paper proposed a large-scale batch clustering method based on multi-frame CT images, which was compared with the traditional clustering methods, and hoped to provide assistance for clinical applications.
Journal ArticleDOI
A multivolume visualization method for spatially aligned volumes after 3D/3D image registration
TL;DR: A new method for the visualization of spatially aligned volumes after 3D/3D image registration is presented, which mixes the data at the earliest level so that it is called the acquisition level intermixing method.
A multi-volume visualization method for spatially aligned volumes after 3D/3D image registration M ethode de fusion et de visualisation de volumes spatialement align es apr es recalage 3D/3D
TL;DR: A new method for the visualization of spatially aligned volumes after 3D/3D image registration is presented, aiming at displaying the full information of the multi-volume in the same scene.
Journal ArticleDOI
Image Segmentation with the EM and the BYY Learning
Kai Tian,Hongzhang Jin +1 more
TL;DR: To address the issue of selection of determining the number of clusters, the Bayesian Ying-Yang (BYY) learning is employed and both intensity and spatial position information are employed as features to describe a pixel in the image.
Journal Article
Edge preserving image segmentation using spatially constrained EM algorithm.
TL;DR: Experimental results obtained by applying the proposed method on synthetic images and simulated brain images demonstrate the improved robustness and effectiveness of the method.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
TL;DR: The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.
Journal Article
A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models
TL;DR: In this paper, the authors describe the EM algorithm for finding the parameters of a mixture of Gaussian densities and a hidden Markov model (HMM) for both discrete and Gaussian mixture observation models.
Journal ArticleDOI
Current methods in medical image segmentation.
TL;DR: A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.
Journal ArticleDOI
Adaptive segmentation of MRI data
TL;DR: Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging data, that has proven to be effective in a study that includes more than 1000 brain scans.