DocumentCode :
846432
Title :
An EM Approach to MAP Solution of Segmenting Tissue Mixtures: A Numerical Analysis
Author :
Liang, Zhengrong ; Wang, Su
Author_Institution :
Depts. of Radiol., State Univ. of New York at Stony Brook, Stony Brook, NY
Volume :
28
Issue :
2
fYear :
2009
Firstpage :
297
Lastpage :
310
Abstract :
This work presents an iterative expectation-maximization (EM) approach to the maximum a posteriori (MAP) solution of segmenting tissue mixtures inside each image voxel. Each tissue type is assumed to follow a normal distribution across the field-of-view (FOV). Furthermore, all tissue types are assumed to be independent from each other. Under these assumptions, the summation of all tissue mixtures inside each voxel leads to the image density mean value at that voxel. The summation of all the tissue mixtures´ unobservable random processes leads to the observed image density at that voxel, and the observed image density value also follows a normal distribution (image data are observed to follow a normal distribution in many applications). By modeling the underlying tissue distributions as a Markov random field across the FOV, the conditional expectation of the posteriori distribution of the tissue mixtures inside each voxel is determined, given the observed image data and the current-iteration estimation of the tissue mixtures. Estimation of the tissue mixtures at next iteration is computed by maximizing the conditional expectation. The iterative EM approach to a MAP solution is achieved by a finite number of iterations and reasonable initial estimate. This MAP-EM framework provides a theoretical solution to the partial volume effect, which has been a major cause of quantitative imprecision in medical image processing. Numerical analysis demonstrated its potential to estimate tissue mixtures accurately and efficiently.
Keywords :
Markov processes; biological tissues; expectation-maximisation algorithm; image segmentation; maximum likelihood estimation; medical image processing; Markov random field; image density mean value; image segmentation; image voxel; iterative expectation-maximization approach; maximum a posteriori method; medical image processing; partial volume effect; tissue mixtures; Biomedical image processing; Clustering algorithms; Gaussian distribution; Image segmentation; Iterative methods; Markov random fields; Numerical analysis; Parameter estimation; Radiology; Random processes; Expectation-maximization (EM) algorithm; maximum a posteriori (MAP) image segmentation; parameter estimation; partial volume effect; tissue mixture fraction; Algorithms; Brain; Computer Simulation; Data Interpretation, Statistical; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Markov Chains; Models, Statistical; Normal Distribution; Phantoms, Imaging;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2008.2004670
Filename :
4608728
Link To Document :
بازگشت