Fast EM Principal Component Analysis Image Registration Using Neighbourhood Pixel Connectivity

Abstract: Image registration (IR) is the systematic process of aligning two images of the same or different modalities. The registration of mono and multimodal images i.e., magnetic resonance images, pose a particular challenge due to intensity non-uniformities (INU) and noise artefacts. Recent similarity measures including regional mutual  information (RMI) and expectation maximisation for principal component analysis with MI (EMPCA-MI) have sought to address this problem. EMPCA-MI incorporates neighbourhood region information to iteratively compute principal components giving superior IR performance compared with RMI, though
it is not always effective in the presence of high INU. This paper presents a modified EMPCA-MI (mEMPCA-MI) similarity measure which introduces
a novel pre-processing step to exploit local spatial information using 4-and 8-pixel neighbourhood connectivity. Experimental results using diverse image datasets, conclusively demonstrate the improved IR robustness of mEMPCA-MI when adopting second-order neighbourhood representations. Furthermore, mEMPCA-MI with 4-pixel connectivity is notably more computationally efficient than EMPCA-MI.
Keywords: Image registration, mutual information, principal component analysis, expectation maximisation algorithms.