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.

Wlamc Scheme To Monomodal Brain Icon Registering

“…The most recent and maximum related works are presented below for better understanding of the discussing techniques. Among the most popular monomodal IR techniques, mutual information (MI) and entropy-based methods [9]- [14] are found to be better performing. The core aspiration of the entropy-based process is to reduce the combined intensity extensive property amid of the figures come to pass registering.…”

Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach

“…In the feature-based group of image registration methods, the accuracy mostly depends on feature matching strategy and control points detection. Inspired by Non-Subsampled Contourlet Transform (NSCT), in 2012, Azzawi et al [ 27 ] implemented a computational method to better detection of salient edges in MRI medical images, and consequently more accurate mono-modal image registration. In 2014, Ghaffari et al [ 28 ] implemented a brand-new similarity measure based on sparse representation in order to deal with non-stationary intensity distortions in mono-modal images.…”

Rigid Point Feature Registration for MRI Brain Images based on Fuzzy Transform

“…Mutual information (MI) is the most popular image similarity measures for registration of multimodality images [18][19][20][21]. The implementation of MI are discussed particularly in [8].…”