DocumentCode :
1404432
Title :
Component Analysis Approach to Estimation of Tissue Intensity Distributions of 3D Images
Author :
Zagorodnov, Vitali ; Ciptadi, Arridhana
Author_Institution :
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
Volume :
30
Issue :
3
fYear :
2011
fDate :
3/1/2011 12:00:00 AM
Firstpage :
838
Lastpage :
848
Abstract :
Many segmentation algorithms in medical imaging rely on accurate modeling and estimation of tissue intensity probability density functions. Gaussian mixture modeling, currently the most common approach, has several drawbacks, such as reliance on a Gaussian model and iterative local optimization used to estimate the model parameters. It also does not take advantage of substantially larger amount of data provided by 3D acquisitions, which are becoming standard in clinical environment. We propose a novel and completely non-parametric algorithm to estimate the tissue intensity probabilities in 3D images. Instead of relying on traditional framework of iterating between classification and estimation, we pose the problem as an instance of a blind source separation problem, where the unknown distributions are treated as sources and histograms of image subvolumes as mixtures. The new approach performed well on synthetic data and real magnetic resonance imaging (MRI) scans of the brain, robustly capturing intensity distributions of even small image structures and partial volume voxels.
Keywords :
Gaussian distribution; biological tissues; biomedical MRI; blind source separation; image segmentation; iterative methods; medical image processing; 3D acquisitions; Gaussian mixture modeling; biomedical MRI scans; blind source separation; brain; image segmentation algorithms; image subvolumes; iterative local optimization; magnetic resonance imaging; partial volume voxels; small image structures; tissue intensity probability density function; Approximation methods; Covariance matrix; Estimation; Histograms; Image segmentation; Noise; Three dimensional displays; Blind source separation; Gaussian mixtures; image segmentation; magnetic resonance imaging (MRI); tissue intensity distributions; Algorithms; Brain; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Biological; Models, Neurological; Pattern Recognition, Automated; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity; Statistical Distributions;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2010.2098417
Filename :
5668506
Link To Document :
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