An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images

TitleAn interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images
Publication TypeJournal Article
Year of Publication2004
AuthorsAbolmaesumi, P., and M. R. Sirouspour
JournalMedical Imaging, IEEE Transactions on
Pagination772 -784
Date Publishedjun.
KeywordsAlgorithms, Automated, Biological, biomedical ultrasonics, cavity boundary, cavity boundary extraction, cavity contours extraction, Computer-Assisted, computerised tomography, dynamical models, equispaced radii, Humans, image enhancement, Image Interpretation, image segmentation, image segmentation technique, interacting multiple model probabilistic data association filter, medical image processing, modelling, Models, Motion, moving object trajectory modelling, Pattern Recognition, probability, Reproducibility of Results, seed point, Sensitivity and Specificity, Statistical, Ultrasonography, ultrasound images

This paper presents a novel segmentation technique for extracting cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity toward its boundary. The distance of the cavity boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius include the measurement of the object position and some false returns. The modeling approach enables us to use the interacting multiple model estimator along with a probabilistic data association filter, for contour extraction. The convergence rate of the method is very fast because it does not employ any numerical optimization. The robustness and accuracy of the method are demonstrated by segmenting contours from a series of ultrasound images. The results are validated through comparison with manual segmentations performed by an expert. An application of the method in segmenting bone contours from computed tomography images is also presented.


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