Optimal Algorithm Selection in Multimodal Medical Image Registration (original) (raw)
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Algorithm Selection in Multimodal Medical Image Registration
IRA-International Journal of Applied Sciences (ISSN 2455-4499), 2020
Over the past few decades, fast-growth has occurred in the area of medical image acquisition devices, and physicians now rely on the utilization of medical images for the diagnosis, treatment plans, and surgical guidance. Researchers have classified medical images according to two structures: anatomical and functional structures. Due to this classification, the data obtained from two or more images of the same object frequently provide complementary and more abundant information through a process known as multimodal medical model registration. Image registration is spatially mapping the coordinate system of the two images obtained from a different viewpoint and utilizing various sensors. Several automatic multimodal medical image registration algorithms have been introduced based on types of medical images and their applications to increase the reliability, robustness, and accuracy. Due to the diversity in imaging and the different demands for applications, there is no single registration algorithm that can do that. This paper introduces a novel method for developing a multimodal medical image registration system that can select the most accepted registration algorithm from a group of registration algorithms for a variety of input datasets. The method described here is based on a machine learning technique that selects the most promising candidate. Several experiments have been conducted, and the results reveal that the novel approach leads to considerably faster reliability, accuracy, and more robustness registration algorithm selection.
Multimodal Medical Data Registration
Abstract Image registration is the task of aligning two images by providing a spatial mapping from one image to the other. This state-of-the-art report aims to give an overview of medical image registration by presenting the registration pipeline and it's different components. Several transformation models (rigid, non-rigid), similarity metrics (sum of squared differences, mutual information, normalized gradient fields) and optimizer algorithms (gradient descent, bfgs) will be presented.
Novel techniques for registration of multimodal medical images
2018
Medical image registration is a critical image processing task in many applications such as image-guided surgery (IGS) and image-guided radiotherapy. Herein, a novel automatic inter-modal affine registration technique is proposed based on the correlation ratio (CR) similarity metric firstly. The technique is demonstrated through registering intra-operative ultrasound (US) scans with magnetic resonance (MR) images of 22 patients from a publicly available database. By using landmark-based mean target registration errors (mTRE) for evaluation, the technique has achieved a result of 2.79$\pm$1.13 mm from an initial value of 5.40$\pm$4.31 mm. A nonparametric statistical analysis performed using the Wilcoxon rank sum test shows that there is a significant difference between pre- and post-registration mTREs with a ppp-value of 0.00580.00580.0058. To achieve this result, the MRI was deemed as the fix image ($I_f$) and the US as the moving image ($I_m$) and then ImI_mIm was transformed to align with $I_...
State-of-the-Art Medical Image Registration Methodologies: A Survey
Almost all computer vision applications, from remote sensing and cartography to medical imaging and biometrics, use image registration or alignment techniques that establish spatial correspondence (one-to-one mapping) between two or more images. These images depict either one planar (2-D) or volumetric (3-D) scene or several such scenes and can be taken at different times, from various viewpoints, and/or by multiple sensors. In medical image processing and analysis, the image registration is instrumental for clinical diagnosis and therapy planning, e.g., to follow disease progression and/or response to treatment, or integrate information from different sources/modalities to form more detailed descriptions of anatomical objects-of-interest. The unified registration goal – aligning a 2-D or 3-D target (sensed) image with a reference image – is reached by specifying a mathematical model of image transformations for and determining model parameters of the desired alignment. Frequently, the parameters provide an optimum of a goal function supported by the parameter space, so that the registration reduces to a certain optimization problem. This chapter overviews the 2-D and the 3-D medical image registration with special reference to the state-of-the-art robust techniques proposed for the last decade and discusses their advantages, drawbacks, and practical implementations.
Medical images are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and mon-itoring disease progression. Within medical research (e.g. neu-roscience research) they are used to investigate disease processes and understand normal development and ageing. Technically, medical imaging mainly processes missing, ambiguous, comple-mentary, redundant and distorted data. In this paper, we pro-pose a set of MR-CT image registration methods by using spatial models like rigid, affine and projective transformations. The reg-istered and fused image contains the properties and details of both MR and CT images and can efficiently be used in clinical medicine.
A Survey of Medical Image Registration
The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is either based on segmented points or surfaces, or on techniques endeavoring to use the full information content of the images involved. .nl) a Also formerly and popularly CAT, computed axial tomography. b Also referred to as NMR, nuclear magnetic resonance, spin imaging, and various other names. c Also echo(graphy). tomography d ), PET (positron emission tomography e ), which together make up the nuclear medicine imaging modalities, and fMRI (functional MRI). With a little imagination, spatially sparse techniques like, EEG (electro encephalography), and MEG (magneto encephalography) can also be named functional imaging techniques. Many more functional modalities can be named, but these are either little used, or still in the pre-clinical research stage, e.g., pMRI (perfusion MRI), fCT (functional CT), EIT (electrical impedance tomography), and MRE (magnetic resonance elastography).
A unified feature-based registration method for multimodality images
2004
While mutual information-based methods have become popular for image registration, the question of what underlying feature to use is rarely discussed. Instead, it is implicitly assumed that intensity is the right feature to be matched. We depart from this tradition by first beginning with a set of feature images-the original intensity image and three directional derivative feature images. This "feature extraction" is performed on both images in a typical intermodality registration setup. Assuming the existence of a training set of registered images, we find the best projection onto a single feature image by maximizing the normalized mutual information (NMI) between the two images w.r.t. the projection weights. After discovering the best feature to match using normalized mutual information as the criterion, we use the same projection coefficients on new test images. We show that affine NMI-based registration of the test images using the new best "feature" is more noise resistant than using image intensity as the default feature. Since the assumption of a registered, training set of images is problematic, we extend the idea to the bootstrap case, wherein we use imperfectly registered images (obtained by using NMI on the original intensity pair) as a training set. The best feature combination is computed using the imperfectly registered pair of images. We show that subsequent NMI-based registration of the best feature image pair is able to improve upon the original imperfect registration. Results are shown on 2D coronal, axial and sagittal slices drawn from a 3D MRI volume of proton density (PD) and T2-weighted images.
Robust Registration of Multi-modal Images: Towards Real-Time Clinical Applications
2002
High performance computing has become a key step to introduce computer tools, like real-time registration, in the medical field. To achieve real-time processing, one usually simplifies and adapts algorithms so that they become application and data specific. This involves designing and programming work for each application, and reduces the generality and robustness of the method. Our goal in this paper is to show that a general registration algorithm can be parallelized on an inexpensive and standard parallel architecture with a mall amount of additional programming work, thus keeping intact the algorithm performance. For medical applications, we show that a cheap cluster of dual-processor PCs connected by an Ethernet network is a good trade-off between the power and the cost of the parallel platform. Portability, scalability and safety requirements led us to choose OpenMP to program multi-processor machines and MPI to coordinate the different nodes of the cluster. The resulting computation times are very good on small and medium resolution images, and they are still acceptable on high resolution MR images (resp. 19, 45 and 95 seconds on 5 dual-processors Pentium III 933 MHz).
Survey of Medical Image Registration
Journal of Biomedical Engineering and Technology, 2013
Computerized Image Registration approaches can offer automatic and accurate image alignments without extensive user involvement and provide tools for visualizing combined images. The aim of this survey is to present a review of publications related to Medical Image Registration. This paper paints a comprehensive picture of image registration methods and their applications. This paper is an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of Medical Image Registration.
Efficient Multimodal Registration Using Least-Squares
Multimodal image registration is a difficult problem in both medical imaging and remote sensing. The least-squares cost function has generally been overlooked for multimodal registration problems due to an underlying assumption that the two images being registered must have corresponding intensities. More recently, methods that employ the least-squares cost function have been developed to efficiently evaluate the globally optimal shift and intensity remapping simultaneously. However, these methods estimate the translation and not the rotation. In this paper we propose a method for using the least-squares cost function efficiently for multimodal registration. By modeling rotation using a linear approximation, we find the globally optimal translation and intensity remapping, and locally optimal rotation angle. In a series of experiments based on registering PD-, T1-, and T2-weighted magnetic resonance images, our method performs better than mutual information.