Fast EM Principal Component Analysis Image Registration Using Neighbourhood Pixel Connectivity

<p><strong>Abstract:</strong> 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<br>it is not always effective in the presence of high INU. This paper presents a modified EMPCA-MI (mEMPCA-MI) similarity measure which introduces<br>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.<br><strong>Keywords:</strong> Image registration, mutual information, principal component analysis, expectation maximisation algorithms.