Design and Analysis of Robust Image Registration Schemes using Machine Learning Algorithms

Abstract

Medical image registration plays a vital role in image-guided interventions to improve clinical decision-making, better visualization, and quantification of anatomical structures. Image registration is the process of transforming different sets of data into one coordinate system. Designing a universal framework for image registration is a challenging task due to the diversity of medical images and various forms of degradation that can occur during acquisition. A registration algorithm, rigid or deformable, aligns images within a common coordinate system to enhance their accuracy for diagnosis, monitoring, and treatment of medical conditions. This precise alignment facilitates clearer analysis and improved clinical decision-making. This research seeks to develop schemes that can effectively capture wide range of image registration scenarios by incorporating both rigid and deformable(non-rigid) registration techniques. Rigid registration is optimized using meta-heuristic methods to adjust rigid transformation parameters while deep learning is employed for feature extraction. In the deformable approach, a two-channel image from the moving and fixed images is processed through the U-Net model to generate a displacement field, while B-spline parameters predict and refine the warped image. The primary objective of this research is to develop a novel approach for rigid and non-rigid medical image registration applicable to both monomodal and multimodal modalities, utilizing machine learning and meta-heuristic optimization techniques. This study includes a thorough review of existing methods and algorithms for medical image registration, aiming to identify and address current gaps and limitations in the field. Firstly, area and feature-based methods are designed for monomodal and multimodal medical image modalities. The proposed approach employs teaching learning-based optimization (TLBO) to optimize rigid transformation parameters such as rotation and translation by considering mutual information (MI) maximization