scholarly journals A Methodology of Three-Dimensional Medical Image Registration Based on Conformal Geometric Invariant

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Liang Hua ◽  
Kean Yu ◽  
Lijun Ding ◽  
Juping Gu ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Subjective Evaluation ◽  
Three Dimensional ◽  
Medical Image Registration ◽  
Geometric Invariant ◽  
Registration Accuracy ◽  
Registration Method ◽  
Cloud Data ◽  
Dual Vector

A three-dimensional multimodality medical image registration method using geometric invariant based on conformal geometric algebra (CGA) theory is put forward for responding to challenges resulting from many free degrees and computational burdens with 3D medical image registration problems. The mathematical model and calculation method of dual-vector projection invariant are established using the distribution characteristics of point cloud data and the point-to-plane distance-based measurement in CGA space. The translation operator and geometric rotation operator during registration operation are built in Clifford algebra (CA) space. The conformal geometrical algebra is used to realize the registration of 3D CT/MR-PD medical image data based on the dual vector geometric invariant. The registration experiment results indicate that the methodology proposed in this paper is of stronger commonality, less computation burden, shorter time consumption, and intuitive geometric meaning. Both subjective evaluation and objective indicators show that the methodology proposed here is of high registration accuracy and suitable for 3D medical image registration.

scholarly journals Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4675 ◽  
Author(s):  
Feng Yang ◽  
Mingyue Ding ◽  
Xuming Zhang
Keyword(s):  
Image Registration ◽  
Similarity Measure ◽  
Energy Function ◽  
Medical Image ◽  
Medical Image Registration ◽  
Self Similarity ◽  
Registration Accuracy ◽  
Registration Method ◽  
Rigid Deformation ◽  
Transformation Parameters

The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.

Use of Information Discrepancy Measure to Register Medical Images

2011 ◽  
Vol 50-51 ◽  
pp. 790-793
Author(s):  
Shao Yan Sun ◽  
Lei Chen
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Medical Image ◽  
Numerical Experiments ◽  
Three Dimensional ◽  
Registration Accuracy ◽  
Registration Method ◽  
Mathematical Properties ◽  
Discrepancy Measure ◽  
Multiple Sequences

Function of Degree of Disagreement (FDOD), a new measure of information discrepancy, quantifies the discrepancy of multiple sequences. This function has some peculiar mathematical properties, such as symmetry, boundedness and monotonicity. In this contribution, we first introduce the FDOD function to solve the three-dimensional (3-D) medical image registration problem. Numerical experiments illustrate that the new registration method based on the FDOD function can obtain subvoxel registration accuracy, and it is a competitive method with the mutual information based method.

A Novel Medical Image Registration Based on Contour Features and MultiScale Information

2014 ◽  
Vol 513-517 ◽  
pp. 3020-3023
Author(s):  
Yun Feng Yang ◽  
Cheng Xin Lin ◽  
Peng Xiao Wang ◽  
Jia Li ◽  
Bo Li
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Clinical Medicine ◽  
Computational Time ◽  
Medical Image Registration ◽  
Canny Operator ◽  
Registration Method ◽  
Contour Lines ◽  
Principal Axes ◽  
Edge Features

Medical image registration is the important technique in the clinical medicine field. A novel hierarchical registration method of the medical images based on multiscale information and contour line is proposed in the paper. At First, contour lines of the couple images are extracted based on the edge features obtained by Canny operator, and contour lines of the couple images are resample in order to reduce the calculation cost in the registration process. Secondly, the Principal Axes method is used to accomplish the rough registration based on the resampled contour lines. Thirdly, multiscale image serials obtained by down-sample transform are used to accomplish the couple images fine registration. Experiment results show that the method not only can achieve more accurate registration results, but also can reduce the computational time greatly. The accurate registration results also can be achieved in the noisy environment.

MEDICAL IMAGE REGISTRATION BASED ON IMPROVED FUZZY C-MEANS CLUSTERING

2015 ◽  
Vol 27 (04) ◽  
pp. 1550032 ◽  
Author(s):  
Meisen Pan ◽  
Jianjun Jiang ◽  
Fen Zhang ◽  
Qiusheng Rong
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Image Registration ◽  
Registration Accuracy ◽  
Local Optima ◽  
Fuzzy C Means ◽  
Straight Line ◽  
Fuzzy C Means Clustering ◽  
Simple Implementation ◽  
Image Registrations

The mutual information (MI) technology and the iterative closest point (ICP) algorithm, as intensity-based and feature-based image registration methods respectively, are commonly put into use in medical image registration. But some naturally existing things which restrict the further development need to be faced and be solved. On one hand, they remain heavy calculation costs and low registration efficiencies. On the other hand, since they seriously depend on whether the initial rotation and translation registration parameters can be exactly selected, they often trap in the local optima and even fail to register images. In this paper, we compute the centroids of the reference and floating images by using the image moments to obtain the initial translation values, and use improved fuzzy C-means clustering (IFCM) to classify the image coordinates. Before clustering, this proposed method first centralizes the medical image coordinates, creates the two-row coordinate matrix to construct the two-dimensional (2D) sample set partitioned into two classes, and computes the slope of a straight line fitted to the two classes, finally derives the rotation angle from solving the arc tangent of the slope and obtains the initial rotation values. The experimental results show that, this proposed method has a fairly simple implementation, a low computational load, a fast registration and good registration accuracy. Also, it can efficiently avoid trapping in the local optima and meets both mono-modality and multi-modality image registrations.

scholarly journals Bayesian Fully Convolutional Networks for Brain Image Registration

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kunpeng Cui ◽  
Panpan Fu ◽  
Yinghao Li ◽  
Yusong Lin
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Medical Image ◽  
Medical Image Registration ◽  
Bayesian Neural Network ◽  
Registration Accuracy ◽  
Change Analysis ◽  
Geometric Transformations ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.

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