title: A region merging prior for variational level set image segmentation.
abstract: In current level set image segmentation methods, the number of regions is assumed to known beforehand. As a result, it remains constant during the optimization of the objective functional. How to allow it to vary is an important question which has been generally avoided. This study investigates a region merging prior related to regions area to allow the number of regions to vary automatically during curve evolution, thereby optimizing the objective functional implicitly with respect to the number of regions. We give a statistical interpretation to the coefficient of this prior to balance its effect systematically against the other functional terms. We demonstrate the validity and efficiency of the method by testing on real images of intensity, color, and motion.
authors.affiliation: Institut National de la Recherche Scientifique (INRSEMT), Montréal, QC, H5A 1K6, Canada. ismail.benayed@ge.com
authors.forename: Ismail
authors.initials: I
authors.lastname: Ben Ayed
authors.forename: Amar
authors.initials: A
authors.lastname: Mitiche
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique