Non-Iterative Hierarchical Registration for Medical Images

Abstract

Digital images from diverse medical imaging modalities and from different imaging times are becoming an indispensable information resource for making clinical decisions. Image registration is an enabling technique for more fully utilizing the embedded heterogeneous image information. However, in addition to the complex differences and deformations inherent in the medical images, the increasing scope, resolution, and dimensionality of imaging pose significant challenges in this medical arena. Wavelets have shown great potential in multi-scale registration due to their superior capacity for representing image information at different resolutions and spatial frequencies. However, the application of wavelets in registration is hindered by their lack of rotation- and translation-invariance. To overcome this obstacle, this paper proposes a non-iterative hierarchical registration method based on points of interest which are extracted automatically from wavelet decompositions. The proposed algorithm for two-dimensional monomodal medical images has been validated by experiments on phantom data and clinical imaging data. This proposed non-iterative method provides a computationally efficient registration, as well as assists in avoiding the non-convergence problem.