Incorporating spatial features with mutual information (MI) has demonstrated superior image registration performance compared with traditional MI-based methods, particularly in the presence of noise and intensity non-uniformities (INU). This paper presents a new efficient MI-based similarity measure which applies Expectation Maximisation for Principal Component Analysis (EMPCA-MI), to afford significantly lower computational complexity, while providing analogous image registration performance with other feature-based MI solutions. Experimental analysis corroborates both the improved robustness and faster runtimes of EMPCA-MI, for different test datasets containing both INU and noise artefacts.
|Title of host publication||19th IEEE International Conference on Image Processing|
|Number of pages||4|
|Publication status||Published - 21 Feb 2013|
|Event||19th IEEE International Conference on Image Processing (ICIP 2012) - Orlando, United States|
Duration: 30 Sep 2012 → 3 Oct 2012
|Conference||19th IEEE International Conference on Image Processing (ICIP 2012)|
|Period||30/09/12 → 3/10/12|