Self-similarity measure for multi-modal image registration
Keyvan Kasiri, Paul Fieguth, David A. Clausi · 2016
In medical image analysis, multi-modal registration has been a challenging task due to the complex intensity relationship between images to be aligned. Conventional multi-modal approaches tend to assess the accuracy of the alignment by measuring a similarity based on statistical dependency of the intensity values between images. However, measuring statistical similarity measures, such as mutual information, is not promising, especially in those cases with complex and spatially dependent intensity relations. A new similarity measure is proposed based on the concept of self-similarity, the similarity of patches within an image, motivated by the fact that similar structures are more probable to undergo similar intensity transformations. The method is applied to the registration framework to align simulated and real brain images from different modalities and compared to the conventional multi-modal registration method. Quantitative evaluation of the method demonstrates that better accuracy can be achieved.