scholarly journals A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaogang Du ◽  
Jianwu Dang ◽  
Yangping Wang ◽  
Song Wang ◽  
Tao Lei
Keyword(s):  
Parallel Computing ◽  
Medical Image ◽  
Medical Images ◽  
Computing Time ◽  
Free Form ◽  
Nonrigid Registration ◽  
Processing Unit ◽  
Registration Accuracy ◽  
B Spline ◽  
Registration Algorithm

The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).

A Non-Rigid Image Registration Algorithm Based on NURBS

2012 ◽  
Vol 170-173 ◽  
pp. 3521-3524
Author(s):  
Jing Jing Wang ◽  
Hong Jun Wang
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Research Work ◽  
Medical Image Processing ◽  
Free Form ◽  
Uniform Grid ◽  
B Spline ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Challenging Research

Non-rigid image registration is an interesting and challenging research work in medical image processing, computer vision and remote sensing fields. In this paper we present a free form deformable algorithm based on NURBS because NURBS (Non-uniform Rational B Spline ) with a non-uniform grid has a higher registration precision and a higher registration speed in comparison with B spline. In our experiment we compare the NURBS based FFD method with the B spline based FFD method quantitatively. The experiment result shows that the algorithm can improve highly the registration precision.

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.

Effect of Measure and Interpolation Methods for Intensity-Based Medical Image Registration

2013 ◽  
Vol 647 ◽  
pp. 612-617
Author(s):  
Guo Dong Zhang ◽  
Xiao Hu Xue ◽  
Wei Guo
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Medical Image ◽  
Medical Image Registration ◽  
Registration Accuracy ◽  
Partial Volume ◽  
Interpolation Methods ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Local Extreme

The local extreme is main reason to hamper the optimization process and influence the registration accuracy in medical image registration algorithm. In general, the accuracy of image registration based on mutual information is afforded by interpolation methods. In this paper, we analyze the effect of the measure and interpolation methods for medical image registration and present a medical image registration algorithm using mutual strictly concave function measure and partial volume (PV) interpolation methods. The experiment results show that for images with low local correlation the algorithm has the ability to reduce the local extreme, the registration accuracy is improved, and the algorithm expended less time than mutual information based registration method with partial volume (PV) or generalized partial volume estimation (GPVE).

Medical Image Registration in Clinical Diagnosis

2019 ◽  
pp. 263-281
Author(s):  
A. Swarnambiga ◽  
Vasuki S.
Keyword(s):  
Image Registration ◽  
Clinical Diagnosis ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Registration ◽  
B Spline ◽  
Conventional Methods

The term medical image covers a wide variety of types of images (modality), especially in medical image registration with very different perspective. In this chapter, spatial technique is approached and analyzed for providing effective clinical diagnosis. The effective conventional methods are chosen for this registration. Researchers have developed and focused this research using proven conventional methods in the respective fields of registration Affine, Demons, and Affine with B-spline. From the overall analysis, it is clear that Affine with B-Spline performs better in registration of medical images than Affine and Demons.

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.

scholarly journals AUTOMATED EXTRACTION OF LIVER OUTLINES FROM COMPUTED TOMOGRAPHY SCAN IMAGES USING A CUDA-BASED SEGMENTATION METHOD

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 31-36
Author(s):  
Y. Chen ◽  
D. Li ◽  
Q. Zhu ◽  
C. Wang ◽  
J. Li
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Computing Time ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Calculation Error ◽  
Processing Unit ◽  
Fast Marching ◽  
Fast Marching Algorithm ◽  
Refinement Algorithm

<p><strong>Abstract.</strong> The traditional fast marching algorithm for segmentation of the liver is suitable for processing on the central processing unit (CPU) platform, however, it is not suitable for implementation on Graphics Processing Unit (GPU). The fuzzy connection algorithm is used to extract the blood vessels in the liver, but there is a calculation error. The refinement algorithm is very time consuming when extracting the target skeleton line from the 3D image. In this paper, the fast-marching algorithm and the thinning algorithm are improved, which can be applied to the GPU computing, The fuzzy algorithm is also improved, and the calculation error of the algorithm is solved, making it more suitable for medical image processing. The computing speed of GPU is far faster than CPU. Medical image processing is one of the earliest applications where the computing performance is improved by GPU. These three segmentation methods, fast marching method, fuzzy connecting method and refinement algorithm are very common in medical image segmentation. Because the increment of medical image data results in the extension of computing time for medical image processing, it is necessary to apply the high parallelism of the GPU to speed up these algorithms. The experiment results demonstrate the feasibility of our accelerating algorithm.</p>

scholarly journals Fast BlockMatching Registration with Entropy-based Similarity

2006 ◽  
Author(s):  
Eduardo Suarez-Santana ◽  
Rafael Nebot ◽  
Carl-fredrik Westin ◽  
Juan Ruiz-Alzola
Keyword(s):  
Input Data ◽  
Source Code ◽  
Similarity Measures ◽  
Block Matching ◽  
Free Form ◽  
Nonrigid Registration ◽  
Local Entropy ◽  
Output Data ◽  
Registration Algorithm ◽  
Algorithm Implementation

This paper describes the implementation of a multidimensional block-matching nonrigid registration algorithm. The main features of the algorithm are its simplicity, its free form nature, the modularity of the similarity measure, which makes it possible using local entropy-based similarity measures and the avoidance of the optimization module. The algorithm implementation described in this paper is based on the method by Suarez et al. This paper, which has already been submitted to the Insight Journal, is accompanied with the source code, input data, parameters and output data used for validating the algorithm described in it.

scholarly journals The TVDMFFDVR Algorithm

2012 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Brian Avants
Keyword(s):  
Free Form ◽  
Command Line ◽  
Characteristic Velocity ◽  
Displacement Fields ◽  
B Spline ◽  
B Splines ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
The Family ◽  
Free Form Deformation

The recent ITKv4 refactoring includes several enhancements to the existing registration framework. These additional transform classes provide access to mappings described by dense displacement fields and their corresponding optimization which complement the popular free-form deformation (FFD) ap- proach already in ITK. Innovation motivated by previous work [5] and recent diffeomorphic image regis- tration developments in which the characteristic velocity field is represented by spatiotemporal B-splines [2], resulted in a diffeomorphic B-spline-based image registration algorithm combining and extending these techniques which we make available in ITK through the gerrit system. Additionally, we include two command line tools showcasing the new elements of the registration refactoring for 1) computing mappings between two images (antsRegistration) including the family of transforms discussed in this article and 2) applying those transformations to images (antsApplyTransforms). NB: The user must download the patch available at http://review.source.kitware.com/#/c/3606/ in or- der to compile the code accompanying this article.

scholarly journals Transitively Consistent and Unbiased Multi-Image Registration using Numerically Stable Transformation Synchronisation

2015 ◽  
Author(s):  
Florian Bernard ◽  
Johan Thunberg ◽  
Andreas Husch ◽  
Luis Salamanca ◽  
Peter Gemmar ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Images ◽  
Desirable Property ◽  
Stable Transformation ◽  
Affine Transformations ◽  
Registration Accuracy ◽  
Brain Images ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Affine Image

Transitive consistency of pairwise transformations is a desirable property of groupwise image registration procedures. The transformation synchronisation method (Bernard et al., 2015) is able to retrieve transitively consistent pairwise transformations from pairwise transformations that are initially not transitively consistent. In the present paper, we present a numerically stable implementation of the transformation synchronisation method for affine transformations, which can deal with very large translations, such as those occurring in medical images where the coordinate origins may be far away from each other. By using this method in conjunction with any pairwise (affine) image registration algorithm, a transitively consistent and unbiased groupwise image registration can be achieved. Experiments involving the average template generation from 3D brain images demonstrate that the method is more robust with respect to outliers and achieves higher registration accuracy compared to reference-based registration.

scholarly journals Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Chang Wang ◽  
Qiongqiong Ren ◽  
Xin Qin ◽  
Yi Yu
Keyword(s):  
Image Registration ◽  
Large Deformation ◽  
Medical Image ◽  
Medical Images ◽  
Nonrigid Registration ◽  
Synthetic Image ◽  
Medical Image Registration ◽  
Evaluation Indexes ◽  
Quantitative Analyses ◽  
Registration Result

Diffeomorphic demons can guarantee smooth and reversible deformation and avoid unreasonable deformation. However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was simulated by rotational distortion and extrusion transform, medical image registration with large deformation was performed, and quantitative analyses were conducted using the registration evaluation indexes, and the influence of different driving forces and parameters on the registration result was analyzed. The registration results of same modality medical images were compared with those obtained using active demons, additive demons, and diffeomorphic demons. Quantitative analyses showed that the proposed method’s normalized cross-correlation coefficient and structural similarity were the highest and mean square error was the lowest. Medical image registration with large deformation could be performed successfully; evaluation indexes remained stable with an increase in deformation strength. The proposed method is effective and robust, and it can be applied to nonrigid registration of same modality medical images with large deformation.

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