title: A hybrid optimization model based on multi-metrics for registration using free-form deformation.
abstract: We propose a new optimization model for non-rigid registration of images using multi-metrics. The ordinary searching step of optimization has been often trapped in local minima and produces wrong registration results. In this paper, if the condition occurs, multi-metrics model will switch to the other metrics to get rid of the local minima, vice versa. We have tested our approach in a variety of experimental conditions and compared the results with the optimization without multi-metrics. The results indicate that the new model is robust and fast in non-rigid registration.
authors.affiliation: Laboratory of Image Guided Surgery and Therapy, (IGST), Shanghai Jiao Tong University (SJTU), China.
authors.forename: Jiasi
authors.initials: J
authors.lastname: Song
authors.forename: Lixu
authors.initials: L
authors.lastname: Gu
authors.forename: Pengfei
authors.initials: P
authors.lastname: Huang
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.forename: Jianrong
authors.initials: J
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D014057:Tomography, X-Ray Computed