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
Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials
TL;DR: In this paper, a segmentation method of mixture models of multivariate Chebyshev orthogonal polynomials for color image was proposed, which does not require any prior assumptions on the models, and it can effectively overcome the problem of model mismatch.
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.