Advanced algorithmic approaches to medical image segmentation
A multiresolution diffused expectation-maximization algorithm for medical image segmentation
Computers in Biology and Medicine
Distributed Markovian segmentation: Application to MR brain scans
Pattern Recognition
A finite mixture model for image segmentation
Statistics and Computing
A Comparative Study on Clustering Algorithms for Multispectral Remote Sensing Image Recognition
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation
Journal of Mathematical Imaging and Vision
A three-level clustering algorithm for color texture segmentation
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
A variational bayes approach to image segmentation
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
An extension of the standard mixture model for image segmentation
IEEE Transactions on Neural Networks
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
EURASIP Journal on Advances in Signal Processing
Image segmentation via coherent clustering in L*a*b* color space
Pattern Recognition Letters
Segmentation of brain images using adaptive atlases with application to ventriculomegaly
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Unsupervised multiresolution segmentation of SAR imagery based on region-based hierarchical model
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Spatially variant mixtures of multiscale ARMA model for SAR imagery segmentation
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
An efficient unsupervised mixture model for image segmentation
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Unsupervised color images segmentation using spatial hidden MRF GDPM model
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
A finite mixture model for detail-preserving image segmentation
Signal Processing
Robust non-rigid point registration based on feature-dependant finite mixture model
Pattern Recognition Letters
Unsupervised classification of SAR images using normalized gamma process mixtures
Digital Signal Processing
| Hi-index | 0.01 |
A spatially variant finite mixture model is proposed for pixel labeling and image segmentation. For the case of spatially varying mixtures of Gaussian density functions with unknown means and variances, an expectation-maximization (EM) algorithm is derived for maximum likelihood estimation of the pixel labels and the parameters of the mixture densities, An a priori density function is formulated for the spatially variant mixture weights. A generalized EM algorithm for maximum a posteriori estimation of the pixel labels based upon these prior densities is derived. This algorithm incorporates a variation of gradient projection in the maximization step and the resulting algorithm takes the form of grouped coordinate ascent. Gaussian densities have been used for simplicity, but the algorithm can easily be modified to incorporate other appropriate models for the mixture model component densities. The accuracy of the algorithm is quantitatively evaluated through Monte Carlo simulation, and its performance is qualitatively assessed via experimental images from computerized tomography (CT) and magnetic resonance imaging (MRI)