Based Feature with MRF Energy Function for Non Rigid Image Registration

This article presents a Markov Random Field (MRF) coupled energy function designed for non rigid image registration. The proposed function addresses the difficulties associated with accurate image registration while maintaining spatial coherence. It combines intensity based and feature based terms. The intensity based term measures the similarity between pixels in fixed and moved images for registration, while the feature based term uses the Scale-Invariant Feature Transform (SIFT) method, ensuring robust alignment of pixel intensities and structural features between image pairs. Two graph cut optimization algorithms, α-expansion, and αβ-swap, are evaluated to optimize the coupled energy function proposed. Experimental results calculated for all pairs in the FIRE retinal dataset demonstrate that the α-expansion algorithm outperforms the αβ-swap algorithm. The α-expansion algorithm exhibits a lower mean squared error (MSE) indicating that it creates a registered image more similar to the fixed in terms of similarity, and a higher structural similarity index value (SSIM), indicating that the algorithm preserves the important visual features of the image. Furthermore, results confirm that the proposed coupled MRF based registration approach significantly outperforms the traditional intensity based registration method.