Algorithm Selection in Multimodal Medical Image Registration

Abstract

Medical image acquisition technology has improved significantly throughout the last several decades, and clinicians now rely on medical images to diagnose illnesses, and to determine treatment protocols, and surgical planning. Medical images have been divided by researchers into two types of structures: functional and anatomical. Anatomical imaging, such as magnetic resonance imaging (MRI), computed tomography imaging (C.T.), ultrasound, and other systems, enables medical personnel to examine a body internally with great accuracy, thereby avoiding the risks associated with exploratory surgery. Functional (or physiological) imaging systems contain single-photon emission computed tomography (SPECT), positron emission tomography (PET), and other methods, which refer to a medical imaging system for discovering or evaluating variations in absorption, blood flow, metabolism, and regional chemical composition. Notably, one of these medical imaging models alone cannot usually supply doctors with adequate information. Additionally, data obtained from several images of the same subject generally provide complementary information via a process called medical image registration. Image registration may be defined as the process of geometrically mapping one -image’s coordinate system to the coordinate system of another image acquired from a different perspective and with a different sensor. Registration performs a crucial role in medical image assessment because it helps clinicians observe the developing trend of the disease and make proper measures accordingly. Medical image registration (MIR) has several applications: radiation therapy, tumour diagnosis and recognition, template atlas application, and surgical guidance system. There are two types of registration: manual registration and registration-based computer system. Manual registration is when the radiologist /physician completes all registration tasks interactively with visual feedback provided by the computer system, which can result in serious problems. For instance, investigations conducted by two experts are not identical, and registration correctness is determined by the user's assessment of the relationship between anatomical features. Furthermore, it may take a long time for the user to achieve proper alignment, and the outcomes vary according to the user. As a result, the outcomes of manual alignment are doubtful and unreliable. The second registration approach is computer-based multimodal medical image registration that targets various medical images, and an arraof application types. . Additionally, automatic registration in medical pictures matches the standard recognized characteristics or voxels in pre- and intra-operative imaging without user input. Registration of multimodal pictures is the initial step in integrating data from several images. Automatic image processing has emerged to mitigate (Husein, do you mean “mitigate” or “improve”?) the manual image registration reliability, robustness, accuracy, and processing time. While such registration algorithms offer advantages when applied to some medical images, their use with others is accompanied by disadvantages. No registration technique can outperform all input datasets due to the changeability of medical imaging and the diverse demands of applications. However, no algorithm is preferable under all possible conditions; given many available algorithms, choosing the one that adapts the best to the task is vital. The essential factor is to choose which method is most appropriate for the situation. The Algorithm Selection Problem has emerged in numerous research disciplines, including medical diagnosis, machine learning, optimization, and computations. The choice of the most powerful strategy for a particular issue seeks to minimize these issues. This study delivers a universal and practical framework for multimodal registration algorithm choice. The primary goal of this study is to introduce a generic structure for constructing a medical image registration system capable of selecting the best registration process from a range of registration algorithms for various used datasets. Three strategies were constructed to examine the framework that was created. The first strategy is based on transforming the problem of algorithm selection into a classification problem. The second strategy investigates the effect of various parameters, such as optimization control points, on the optimal selection. The third strategy establishes a framework for choosing the optimal registration algorithm for a delivered dataset based on two primary criteria: registration algorithm applicability, and performance measures. The approach mentioned in this section has relied on machine learning methods and artificial neural networks to determine which candidate is most promising. Several experiments and scenarios have been conducted, and the results reveal that the novel Framework strategy leads to achieving the best performance, such as high accuracy, reliability, robustness, efficiency, and low processing time.