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Image Registration Based on Mutual Information and Corner Points


计算机系统应用 , 2012,
Abstract: Registration based on mutual information is a typical method in medical image registration. Mutual information is a common similarity measure in image registration, which has excellent robustness and accuracy, but large calculation amount makes it difficult to be applied to clinics. A maximization of mutual information based image registration method is described. Firstly Because of using maximum mutual information to image registration have inferiority, the registration based on local curvature maximum to obtain corner points. Then, the one to one matching points could be obtained through mutual information rough match. Experimental results indicate the proposed algorithm can achieve better accuracy and good robust.
Dejan Toma?evi?,Bo?tjan Likar,Franjo Pernu?
Image Analysis and Stereology , 2012, DOI: 10.5566/ias.v31.p43-53
Abstract: Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.
Spatial Information Based Medical Image Registration using Mutual Information  [cached]
Benzheng Wei,Zhimin Zhao,Xin Peng
Journal of Multimedia , 2011, DOI: 10.4304/jmm.6.3.236-243
Abstract: Image registration is a valuable technique for medical diagnosis and treatment. Due to the inferiority of image registration using maximum mutual information, a new hybrid method of multimodality medical image registration based on mutual information of spatial information is proposed. The new measure that combines mutual information, spatial information and feature characteristics, is proposed. Edge points are used as features, obtained from a morphology gradient detector. Feature characteristics like location, edge strength and orientation are taken into account to compute a joint probability distribution of corresponding edge points in two images. Mutual information based on this function is minimized to find the best alignment parameters. Finally, the translation parameters are calculated by using a modified Particle Swarm Optimization (MPSO) algorithm. The experimental results demonstrate the effectiveness of the proposed registration scheme.
Registration of Images with Outliers Using Joint Saliency Map  [PDF]
Binjie Qin,Zhijun Gu,Xianjun Sun,Yisong Lv
Computer Science , 2013, DOI: 10.1109/LSP.2009.2033728
Abstract: Mutual information (MI) is a popular similarity measure for image registration, whereby good registration can be achieved by maximizing the compactness of the clusters in the joint histogram. However, MI is sensitive to the "outlier" objects that appear in one image but not the other, and also suffers from local and biased maxima. We propose a novel joint saliency map (JSM) to highlight the corresponding salient structures in the two images, and emphatically group those salient structures into the smoothed compact clusters in the weighted joint histogram. This strategy could solve both the outlier and the local maxima problems. Experimental results show that the JSM-MI based algorithm is not only accurate but also robust for registration of challenging image pairs with outliers.
A Combined Intensity and Gradient-Based Similarity Criterion for Interindividual SPECT Brain Scan Registration  [cached]
Lundqvist Roger,Bengtsson Ewert,Thurfjell Lennart
EURASIP Journal on Advances in Signal Processing , 2003,
Abstract: An evaluation of a new similarity criterion for interindividual image registration is presented. The proposed criterion combines intensity and gradient information from the images to achieve a more robust and accurate registration. It builds on a combination of the normalised mutual information (NMI) cost function and a gradient-weighting function, calculated from gradient magnitude and relative gradient angle values from the images. An investigation was made to determine the best settings for the number of bins in the NMI joint histograms, subsampling, and smoothing of the images prior to the registration. The new method was compared with the NMI and correlation-coefficient (CC) criterions for interindividual SPECT image registration. Two different validation tests were performed, based on the displacement of voxels inside the brain relative to their estimated true positions after registration. The results show that the registration quality was improved when compared with the NMI and CC measures. The actual improvements, in one of the tests, were in the order of 30-40% for the mean voxel displacement error measured within 20 different SPECT images. A conclusion from the studies is that the new similarity measure significantly improves the registration quality, compared with the NMI and CC similarity measures.
Xiaoxiang Wang,Jie Tian
Image Analysis and Stereology , 2005, DOI: 10.5566/ias.v24.p1-7
Abstract: Herein one proposes a mutual information-based registration method using pixel gradient information rather than pixel intensity information. Special care is paid to finding the global maximum of the registration function. In particular, one uses simulated annealing method speeded up by including a statistical analysis to reduce the next search space across the cooling schedule. An additional speed up is obtained by combining this numerical strategy with hill-climbing method. Experimental results obtained on a limited database of biological images illustrate that the proposed method for image registration is relatively fast, and performs well as the overlap between the floating and reference images is decreased and/or the image resolution is coarsened.
Application of generalized Jensen-Schur measure in medical image registration

HU Shun-bo,WANG Guang-tai,LIU Chang-chun,SHAO Peng,

计算机应用 , 2009,
Abstract: For the influences of noise, interpolation and image modality, the medical image registration method based on mutual information or normalized mutual information would cause local extrema, small convergence area, and even inaccurate registration. A new generalized Jensen-Schur measure was defined, which used "nonlinear increasing" of butterworth function to eliminate false extrema. Four new generalized Jensen-Schur measures, mutual information and normalized mutual information were analyzed and compared by applying them to rigid registration. The results of tests show that the new constructed JS22 and JS23 measures outperform other measures in noise immunity and convergence, and eliminating false extrema caused by PV interpolation.
ZKIP and Mutual Information as Measure on Image Analysis
S. Samundeeswari,M. Thiyagarajan
Journal of Engineering and Applied Sciences , 2012, DOI: 10.3923/jeasci.2010.290.295
Abstract: The researchers try to extract the component image virtually hidden in the principle image. Information theoretic quantities are used to measure similarity of images in a statistical frame work. Statistical method describes the texture in a form suitable for statistical pattern recognition. This is suggested from large primitives and fine structure of smaller primitives. Co-occurrence matrix method is used to edge frequency detection and separation. Here, we appeal to the mutual information to bring out the component image from a complete image presentation by statistical zero knowledge interactive protocol (statistical ZKIP).
A Novel Image Registration Method Combining Morphological Gradient Mutual Information with Multiresolution Optimizer


自动化学报 , 2008,
Abstract: An improved image registration method is proposed based on mutual information.Firstly,a new registration measure function,combining mutual information with morphological gradient information of the images,is used to take the advantages of these two different indices to achieve the global optimization.Secondly,a hybrid optimizer based on particle swarm optimization(PSO)and Powell is applied to efficiently restrain local maximums of our new registration measure function.Lastly,multiresolution data structure based on wavelet transform is used to expedite the registration process and increase the algorithm's robustness.Experimental results demonstrate that this new algorithm can efficiently yield a good registration result and can achieve subvoxel accuracy,while meeting the real-time need of diagnostic and research purposes.
Multi-Modality Medical Image Registration Basedon Maximization of Mutual Information

LUO Shu-qian,LI Xiang,
,李 响

中国图象图形学报 , 2000,
Abstract: In this paper a maximization of mutual information based multi -modality medical image registration method is described. The method presented in this paper applies mutual information to measure the information redundancy b etween the intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. MI is used as a measure o f similarity of two images. There exist many important technical issues to be so lved about the method such as how to compute MI more accurately and how to obtai n the maximization of MI, which are seldom mentioned in published papers. In thi s paper we provide some implementation issues, for example, subsampling, PV inte rpolation, outlier strategy. Powell searching algorithm is used which does not c ompute gradients. The combination of these computation techniques and searching strategy leads to a fast and accurate multi-modality image registration. The re gistration results of 3D human brain volume data of 41 CT-MR and 35 PET-MR fro m seven patients are validated to be subvoxel. The registration method is promis ing in clinical use.
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