A Hybrid Medical Image Registration Framework Integrating Fiducial Markers and Wavelet-Based Mutual Information - Zineb Bensalem; Zoubeida Messali; Mecheri Kious3; Messaouda Laarbi (International Journal of Computational and Experimental Science and Engineering, 2026)
This paper proposes a hybrid medical image registration framework aimed at improving the alignment accuracy of multimodal brain MRI images. The proposed approach integrates the geometric robustness of fiducial markers with the multi-resolution frequency analysis capability of the Daubechies wavelet transform (db2). Initially, three artificial circular fiducial markers are placed at stable anatomical landmarks and automatically detected using the Circular Hough Transform with radii ranging from 6 to 20 pixels, enabling reliable estimation of an initial affine transformation and reducing gross alignment errors. Subsequently, a two-dimensional Discrete Wavelet Transform (DWT) is applied to the reference and moving images, where the low-frequency (LL) sub-bands are exploited to perform fine registration using Mutual Information (MI) as the similarity metric. This frequency-domain refinement enhances robustness against noise and intensity variations. Experimental evaluations on healthy and tumor-affected brain MRI datasets demonstrate that the proposed hybrid framework outperforms conventional intensity-based methods relying on MSE, SSIM, and PSNR, particularly in challenging pathological scenarios.