title: Automatic active model initialization via Poisson inverse gradient.
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.
authors.affiliation: C.L. Brown Department of Electrical and Computer Engineering/Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA. bingli@virginia.edu
authors.forename: Bing
authors.initials: B
authors.lastname: Li
authors.forename: Scott T
authors.initials: ST
authors.lastname: Acton
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D016012:Poisson Distribution; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity