MRI Monomodal Feature-Based Registration Based on the Efficiency of Multiresolution Representation and Mutual Information

Abstract

Image registration methods based on mutual information criteria have been widely used in monomodal medical image registration and have shown promising results. Feature-based registration is an efficient technique for clinical use, because it can significantly reduce computational costs. In general, the majority of registration methods consist of the following four steps: feature extraction, feature matching, transformation of the models and, finally, resampling the image. It was noted that the accuracy of the registration process depends on matching a feature and control points (CP) detection. Therefore in this paper has been to rely on this feature for magnetic resonance image (MRI) monomodal registration. We have proposed to extract the salient edges and extracted a CP of medical images by using efficiency of multiresolution representation of data nonsubsampled contourlet transform (NSCT). The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information (MI) was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). We implement experiments to evaluate the performance of the NTSC and MI similarity measures for 2-D monomodal registration. The experimental results showed that the proposed method produces totally accurate performance for MRI monomodal registration.