title: Registration of microscopic iris image sequences using probabilistic mesh.
abstract: This paper explores the use of deformable mesh for registration of microscopic iris image sequences. The registration, as an effort for stabilizing and rectifying images corrupted by motion artifacts, is a crucial step toward leukocyte tracking and motion characterization for the study of immune systems. The image sequences are characterized by locally nonlinear deformations, where an accurate analytical expression can not be derived through modeling of image formation. We generalize the existing deformable mesh and formulate it in a probabilistic framework, which allows us to conveniently introduce local image similarity measures, to model image dynamics and to maintain a well-defined mesh structure and smooth deformation through appropriate regularization. Experimental results demonstrate the effectiveness and accuracy of the algorithm.
authors.affiliation: Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health and Science University, USA. xubosong@csee.ogi.edu
authors.forename: Xubo B
authors.initials: XB
authors.lastname: Song
authors.forename: Andriy
authors.initials: A
authors.lastname: Myronenko
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Plank
authors.forename: James T
authors.initials: JT
authors.lastname: Rosenbaum
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007498:Iris / Q000166:cytology*; D018715:Microscopy, Video / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009887:Ophthalmoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique