scholarly journals Algorithm Selection in Multimodal Medical Image Registration

2020 ◽  
Vol 14 (2) ◽  
pp. 10
Author(s):  
Husein Elkeshreu ◽  
Otman Basir
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Images ◽  
Medical Model ◽  
Medical Image Registration ◽  
Algorithm Selection ◽  
Registration System ◽  
Surgical Guidance ◽  
Registration Algorithm ◽  
Novel Approach

Over the past few decades, fast<strong>-</strong>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<strong> </strong>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.

Medical Image Analysis by Robot Kinematics

2006 ◽  
Vol 326-328 ◽  
pp. 875-878
Author(s):  
Jae Bum An ◽  
Li Li Xin
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Robot Kinematics ◽  
Surgical Robots ◽  
Cross Sectional ◽  
Registration Algorithm ◽  
Straight Line ◽  
Actual Shape

In this paper we present an analysis of medical images based on robot kinematics. One of the most important problems in robot-assisted surgeries is associated with the medical image registration of surgical tools and anatomical targets. The fundamental problems of contemporary frame-based image registration are that the registration fails in case of incomplete data in the image and the registration algorithm depends on the shape, assembly, and number of fiducials. To solve the registration problem in the situation where a cylindrical end-effector of surgical robots operates inside the patient’s body, we developed a numerical method by applying robot kinematics knowledge to cross-sectional medical images. Our method includes a 6-D registration algorithm and a cylindrical frame with four helix and one straight line fiducials. The numerical algorithm requires only a single cross-sectional image and are robust to noise and missing data, and are algorithmically invariant to the actual shape, number, and assembly of fiducials. The algorithm and frame are introduced in this paper, and simulation results are described to show the adequate accuracy and resistance to noise.

2D-3D Medical Image Registration Based on Ant Colony Algorithm

2013 ◽  
Vol 462-463 ◽  
pp. 267-273 ◽  
Author(s):  
Wei Wei ◽  
Wei Lin ◽  
Liang Liu ◽  
Zhong Qin Hu
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Ant Colony Algorithm ◽  
Search Strategy ◽  
Ant Colony ◽  
Medical Image Registration ◽  
X Ray ◽  
Registration Algorithm ◽  
Transmission Parameters ◽  
Optimization Search

Object: To optimize the rigidity registration algorithm between X-ray fluoroscopy and CT, and improve the accuracy of registration. Method: By changing the transmission parameters of the ray tracing, it can obtain the original DRR images and the float DRR image for registration. In trials, it uses ant colony algorithm as the optimized search strategy and Mutual information as the similarity measure. Result: ant colony algorithm and the improved ant colony algorithm compared to the classic Powell algorithm to improve the accuracy of registration about 10% and 20%, achieved good results. Conclusion: Ant Colony Algorithm as optimization search strategy can effectively solve the local minima problem in 2D-3D medical image registration, and further improve the accuracy of registration.

scholarly journals Optimal Algorithm Selection in Multimodal Medical Image Registration

2021 ◽  
Vol 15 (4) ◽  
pp. 55
Author(s):  
Husein Elkeshreu ◽  
Otman Basir
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Optimal Algorithm ◽  
Operating Conditions ◽  
Selection Strategy ◽  
Medical Image Registration ◽  
Control Parameters ◽  
Multiple Sources ◽  
Algorithm Selection ◽  
Wide Range

Many medical applications benefit from the diversity inherent in imaging technologies to obtain more reliable diagnoses and assessments. Typically, the images obtained from multiple sources are acquired at distinct times and from different viewpoints, rendering a multitude of challenges for the registration process. Furthermore, different areas of the human body require disparate registration functional capabilities and degrees of accuracy. Thus, the benefit attained from the image multiplicity hinges heavily on the imaging modalities employed as well as the accuracy of the alignment process.  It is no surprise then that a wide range of registration techniques has emerged in the last two decades. Nevertheless, it is widely acknowledged that despite the many attempts, no registration technique has been able to deliver the required accuracy consistently under diverse operating conditions.  This paper introduces a novel method for achieving multimodal medical image registration based on exploiting the complementary and competitive nature of the algorithmic approaches behind a wide range of registration techniques. First, a thorough investigation of a wide range of registration algorithms is conducted for the purpose of understanding and quantifying their registration capabilities as well as the influence of their control parameters. Subsequently, a supervised randomized machine learning strategy is proposed for selecting the best registration algorithm for a given registration instance, and for determining the optimal control parameters for such algorithm. Several experiments have been conducted to verify the capabilities of the proposed selection strategy with respect to registration reliability, accuracy, and robustness.

scholarly journals Image registration using the Morphon algorithm: an ITK implementation

2009 ◽  
Author(s):  
Jerome Plumat ◽  
Mats Andersson ◽  
Guillaume Janssens ◽  
Jonathan Orban de Xivry ◽  
Hans Knutsson ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Deformation Field ◽  
Medical Image Registration ◽  
Registration Algorithm

Medical image registration is becoming a more and more useful component of a large number of applications. The presented method aims to enrich the ITK library. This method, called Morphon registration algorithm, computes a dense deformation field accepting inputs from different intensity contrasts. This article presents its implementation within the Insight Toolkit. In this paper, we provide a brief description of the algorithm, a presentation of the implementation, the justification of our modified classes and the results given by the algorithm. We demonstrate the algorithm in application of different images intesity constrasts and dimensions.

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