title: Three-dimensional multimodality medical image registration using a parameter accumulation approach.
abstract: A parameter accumulation method based on the Hough transformation is proposed to register three-dimensional (3-D) multimodality medical images. The estimation of registration parameters is decomposed into separate estimations of rotation, using directional vectors, and translation, using positional vectors. Similarly, the rotation parameters are decomposed into the rotation axis and angle, which are then estimated separately. This kind of decomposition reduces the parametric dimension and improves the computing efficiency which has been a major concern in implementing the Hough transformation. When 3-D rotation is involved, evaluating registration error is not straightforward. This paper introduces an equivalent error angle as a criterion to evaluate the performance of 3-D registration methods. Experimental results indicate that a least-squares fitting is superior to the parameter accumulation with data contaminated by additive noise only. When mismatched feature points (outliers) exist in the data set, however, the parameter accumulation approach is more accurate. The application of the proposed approach to the registration of 3-D PET and CT images is demonstrated.
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA.
authors.forename: Q S
authors.initials: QS
authors.lastname: Chen
authors.forename: M
authors.initials: M
authors.lastname: Defrise
authors.forename: F
authors.initials: F
authors.lastname: Deconinck
mesh_terms: D000465:Algorithms; D001699:Biometry; D055592:Biophysical Phenomena; D001703:Biophysics; D001921:Brain / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D008962:Models, Theoretical; D014055:Tomography, Emission-Computed / Q000379:methods / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000379:methods / Q000706:statistics & numerical data