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 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 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.

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).

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.

ANALYSIS OF THE α-RENYI ENTROPY AND ITS APPLICATION FOR MEDICAL IMAGE REGISTRATION

2017 ◽  
Vol 29 (03) ◽  
pp. 1750020
Author(s):  
Meisen Pan ◽  
Fen Zhang
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Parameter Optimization ◽  
Medical Image ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Registration Accuracy ◽  
Computational Load ◽  
Image Registrations ◽  
Parameter Ranges

In this paper, the [Formula: see text]-Renyi entropy and [Formula: see text]-Renyi-based mutual information (RMI) are first introduced. Then the influence of the parameter [Formula: see text] on the curve of the RMI and the computational load of image registration are discussed and analyzed to explore the appropriate parameter ranges. Finally, the RMI with the appropriate parameter [Formula: see text] is viewed as the similarity measure between the reference and floating images. In the experiments, the Simplex method is chosen as the multi-parameter optimization one. The experimental results reveal that the proposed method has low computational load, fast registration and good registration accuracy. It is adapted to both mono-modality and multi-modality image registrations.

Medical Image Registration Combined with SURF and Improved RANSAC Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1233-1237
Author(s):  
Zong Yun Gu ◽  
Chun Min Du ◽  
Li Jin ◽  
Hong Chun Tan
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
High Efficiency ◽  
Least Square Method ◽  
High Accuracy ◽  
Least Square ◽  
Medical Image Registration ◽  
Registration Accuracy ◽  
Large Scope ◽  
Ransac Algorithm

Aiming at the requirements of medical image registration for good robustness, high-accuracy and speed, this paper proposes a Medical Image Registration algorithm Combined SURF with improved RANSAC algorithm. This algorithm first of all extracts SURF featured points from images and matches similar featured points, then the improved RANSAC algorithm is used to eliminate wrong matches, and finally the image registration process is accomplished by estimating space geometric varied parameters according to least square method. The algorithm combines robustness and high efficiency of SURF and high-accuracy of improved RANSAC algorithm. Experimental results show that in the condition of images with noise, non-uniform intensity and large scope of the initial misalignment, the proposed algorithm achieves better robustness and higher speed while maintaining good registration accuracy compared with the conventional ional area-based and feature-based registration methods.

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