title: Particle-guided image registration.
abstract: We present a novel image registration method based on B-spline free-form deformation that simultaneously optimizes particle correspondence and image similarity metrics. Different from previous B-spline based registration methods optimized w.r.t. the control points, the deformation in our method is estimated from a set of dense unstructured pair of points, which we refer as corresponding particles. As intensity values are matched on the corresponding location, the registration performance is iteratively improved. Moreover, the use of corresponding particles naturally extends our method to a group-wise registration by computing a mean of particles. Motivated by a surface-based group-wise particle correspondence method, we developed a novel system that takes such particles to the image domain, while keeping the spirit of the method similar. The core algorithm both minimizes an entropy based group-wise correspondence metric as well as maximizes the space sampling of the particles. We demonstrate the results of our method in an application of rodent brain structure segmentation and show that our method provides better accuracy in two structures compared to other registration methods.
authors: Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
authors: Joohwi
authors: J
authors: Lee
authors: Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
authors: Ilwoo
authors: I
authors: Lyu
authors: Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52246, USA.
authors: Ipek
authors: I
authors: Oğuz
authors: Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
authors: Martin A
authors: MA
authors: Styner
mesh_terms: D000375:Aging / Q000473:pathology*; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000473:pathology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique