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IEEE Trans Image Process. 2008 Aug;17(8):1406-20. doi: 10.1109/TIP.2008.925375.

Automatic active model initialization via Poisson inverse gradient.

Author information

1
C.L. Brown Department of Electrical and Computer Engineering/Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA. bingli@virginia.edu

Abstract

Active models have been widely used in image processing applications. A crucial stage that affects the ultimate active model performance is initialization. This paper proposes a novel automatic initialization approach for parametric active models in both 2-D and 3-D. The PIG initialization method exploits a novel technique that essentially estimates the external energy field from the external force field and determines the most likely initial segmentation. Examples and comparisons with two state-of-the- art automatic initialization methods are presented to illustrate the advantages of this innovation, including the ability to choose the number of active models deployed, rapid convergence, accommodation of broken edges, superior noise robustness, and segmentation accuracy.

PMID:
18632349
DOI:
10.1109/TIP.2008.925375
[Indexed for MEDLINE]

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