A Bayesian Bounded Asymmetric Mixture Model With Segmentation Application

Segmentation of a medical image based on the modeling and estimation of the tissue intensity probability density functions via a Gaussian mixture model has recently received great attention. However, the Gaussian distribution is unbounded and symmetrical around its mean. This study presents a new bounded asymmetric mixture model for analyzing both univariate and multivariate data. The advantage of the proposed model is that it has the flexibility to fit different shapes of observed data such as non-Gaussian, nonsymmetric, and bounded support data. Another advantage is that each component of the proposed model has the ability to model the observed data with different bounded support regions, which is suitable for application on image segmentation. Our method is intuitively appealing, simple, and easy to implement. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood function. Numerical experiments are presented where the proposed model is tested in various images from simulated to real 3- $\hbox{D}$ medical ones.

Authors

• Thanh Minh Nguyen
• Q. M. Jonathan Wu
• Dibyendu Mukherjee
• Hui Zhang

Keywords

• Data models
• Gaussian distribution
• Image segmentation
• Mixture models
• Shape
• Biomedical imaging
• Bayes methods
• Mixture Model
• Asymmetric Model
• Normal Distribution
• Medical Imaging
• Log-likelihood
• Probability Density Function
• Multivariate Data
• Order Parameter
• Unique Data
• Gaussian Mixture Model
• Negative Log-likelihood Function
• Cerebrospinal Fluid
• White Matter
• Experimental Section
• Gray Matter
• Real Applications
• Expectation Maximization
• Weighting Factor
• Dimensional Vector
• Generalized Gaussian Distribution
• Dice Similarity Coefficient
• Error Function
• Value Of Image
• non-Gaussian Data
• Noisy Images
• Segmentation Results
• Problem Of Data
• Bayesian estimation
• bounded support regions
• medical image segmentation
• non-Gaussian
• nonsymmetric
• Algorithms
• Analysis of Variance
• Bayes Theorem
• Brain
• Computer Simulation
• Humans
• Image Processing, Computer-Assisted
• Imaging, Three-Dimensional
• Models, Statistical