Segmentation in Ultrasonic B-Mode Images of Healthy Carotid Arteries Using Mixtures of Nakagami Distributions and Stochastic Optimization

• Published in: IEEE transactions on medical imaging Vol. 28; no. 2; pp. 215 - 229
• Main Authors: Destrempes, F., Meunier, J., Giroux, M.-F., Soulez, G., Cloutier, G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; B -mode; Bayes Theorem; Bayesian model; Blood; Carotid arteries; Carotid Arteries - diagnostic imaging; carotid artery; Distributed computing; Elasticity; expectation maximization (EM) algorithm; exploration selection algorithm; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Maximum a posteriori estimation; Maximum likelihood estimation; mixtures of gamma distributions; mixtures of Nakagami distributions; Models, Statistical; Nakagami distribution; segmentation; stochastic optimization; Stochastic Processes; Studies; Ultrasonic imaging; Ultrasonography - methods; ultrasound; Exploration Selection algorithm; B-mode; Bayesian model; mixtures of gamma distributions; segmentation; Expectation Maximization algorithm; carotid artery; ultrasound; mixtures of Nakagami distributions; stochastic optimization
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2008.929098

The goal of this work is to perform a segmentation of the intimamedia thickness (IMT) of carotid arteries in view of computing various dynamical properties of that tissue, such as the elasticity distribution (elastogram). The echogenicity of a region of interest comprising the intima-media layers, the lumen, and the adventitia in an ultrasonic B -mode image is modeled by a mixture of three Nakagami distributions. In a first step, we compute the maximum a posteriori estimator of the proposed model, using the expectation maximization (EM) algorithm. We then compute the optimal segmentation based on the estimated distributions as well as a statistical prior for disease-free IMT using a variant of the exploration/selection (ES) algorithm. Convergence of the ES algorithm to the optimal solution is assured asymptotically and is independent of the initial solution. In particular, our method is well suited to a semi-automatic context that requires minimal manual initialization. Tests of the proposed method on 30 sequences of ultrasonic B -mode images of presumably disease-free control subjects are reported. They suggest that the semi-automatic segmentations obtained by the proposed method are within the variability of the manual segmentations of two experts.