Regularized Quadrature Filters for Local Frequency Estimation: Application to Multimodal Volume Image Registration
Jundong Liu · 2001
Multimodal image registration is a fundamental problem in medical image analysis. In this paper, we propose a novel algorithm to compute the local frequency representations of the multimodal data sets to be registered. Local frequency representation can detect edge and ridge information simultaneously. In this algorithm, we develop regularized quadrature filters (RQFs) to compute local frequency maps, which are relatively insensitivity to noise in comparison to standard QFs. The local frequency maps thus obtained are used as an underlying representation to which a statistically robust matching technique is applied, to estimate a parameterized transformation between the volume data sets. We present experimental results for registering several pairs of CT-MR data sets along with comparisons to other matching methods. 1