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MRI Monomodal Feature-Based Registration Based on the Efficiency of Multiresolution Representation and Mutual Information
American Journal of Biomedical Engineering , 2012, DOI: 10.5923/j.ajbe.20120203.02
Abstract: Image registration methods based on mutual information criteria have been widely used in monomodal medical image registration and have shown promising results. Feature-based registration is an efficient technique for clinical use, because it can significantly reduce computational costs. In general, the majority of registration methods consist of the following four steps: feature extraction, feature matching, transformation of the models and, finally, resampling the image. It was noted that the accuracy of the registration process depends on matching a feature and control points (CP) detection. Therefore in this paper has been to rely on this feature for magnetic resonance image (MRI) monomodal registration. We have proposed to extract the salient edges and extracted a CP of medical images by using efficiency of multiresolution representation of data nonsubsampled contourlet transform (NSCT). The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information (MI) was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). We implement experiments to evaluate the performance of the NTSC and MI similarity measures for 2-D monomodal registration. The experimental results showed that the proposed method produces totally accurate performance for MRI monomodal registration.
MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION  [cached]
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.
IMAGE REGISTRATION BASED ON MAXIMIZATION OF GRADIENT CODE MUTUAL INFORMATION  [cached]
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.
Robust Image Registration Based on Mutual Information Measure  [PDF]
Witold Kosiński, Pawe? Michalak, Piotr Gut
Journal of Signal and Information Processing (JSIP) , 2012, DOI: 10.4236/jsip.2012.32023
Abstract: A new implementation of the image registration algorithm based on the mutual information is presented for the case of medical images. The registration is achieved if the maximum of the mutual information is attained. In this maximization process optimal values of five parameters of an affine transformation are searched.
Anatomical Feature-guided Volumeric Registration of Multimodal Prostate MRI  [PDF]
Xin Zhao,Arie Kaufman
Computer Science , 2013,
Abstract: Radiological imaging of prostate is becoming more popular among researchers and clinicians in searching for diseases, primarily cancer. Scans might be acquired at different times, with patient movement between scans, or with different equipment, resulting in multiple datasets that need to be registered. For this issue, we introduce a registration method using anatomical feature-guided mutual information. Prostate scans of the same patient taken in three different orientations are first aligned for the accurate detection of anatomical features in 3D. Then, our pipeline allows for multiple modalities registration through the use of anatomical features, such as the interior urethra of prostate and gland utricle, in a bijective way. The novelty of this approach is the application of anatomical features as the pre-specified corresponding landmarks for prostate registration. We evaluate the registration results through both artificial and clinical datasets. Registration accuracy is evaluated by performing statistical analysis of local intensity differences or spatial differences of anatomical landmarks between various MR datasets. Evaluation results demonstrate that our method statistics-significantly improves the quality of registration. Although this strategy is tested for MRI-guided brachytherapy, the preliminary results from these experiments suggest that it can be also applied to other settings such as transrectal ultrasound-guided or CT-guided therapy, where the integration of preoperative MRI may have a significant impact upon treatment planning and guidance.
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.
N-dimensional Multimodality Medical Images Registration Based on Mutual Information
基于互信息的N维多模医学图像配准

LIU Qing,GUO Xi-juan,XU Shen-yang,
刘晴
,郭希娟,许慎洋

中国图象图形学报 , 2009,
Abstract: At present, the multimodality medical image registration has been all confined in registering two images and rarely involved N-dimensional images (three and more than three dimensions). Using the expanded N-dimensional mutual information measure (E-NMIM) to register multiple images inefficient, and cannot meet the clinical requirement.In addition mutual information(MI) values are not necessarily nonnegative. In this paper, we introduce a new N-dimensional mutual information measure (N-NMIM), which can ensure MI values are nonnegative, bounded to range from 1 to 2. At the same time, the rate of the registration has moved up. Then this definition is tested and proved to be effective on registration of three lumbar vertebra images through simulation, including CT,T1 weighted MRI and T2 weighted MRI.
An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: preliminary results
Xia Li, Richard G Abramson, Lori R Arlinghaus, Anuradha Chakravarthy, Vandana Abramson, Ingrid Mayer, Jaime Farley, Dominique Delbeke, Thomas E Yankeelov
EJNMMI Research , 2012, DOI: 10.1186/2191-219x-2-62
Abstract: The goal is to optimally register normal tissues while simultaneously preventing tumor distortion. In order to accomplish this, we constructed a PET support device to enable PET/CT imaging of the breasts of ten patients in the prone position and applied a mutual information-based rigid body registration followed by a non-rigid registration. The non-rigid registration algorithm extended the adaptive bases algorithm (ABA) by incorporating a tumor volume-preserving constraint, which computed the Jacobian determinant over the tumor regions as outlined on the PET/CT images, into the cost function. We tested this approach on ten breast cancer patients undergoing neoadjuvant chemotherapy.By both qualitative and quantitative evaluation, our constrained algorithm yielded significantly less tumor distortion than the unconstrained algorithm: considering the tumor volume determined from standard uptake value maps, the post-registration median tumor volume changes, and the 25th and 75th quantiles were 3.42% (0%, 13.39%) and 16.93% (9.21%, 49.93%) for the constrained and unconstrained algorithms, respectively (p?=?0.002), while the bending energy (a measure of the smoothness of the deformation) was 0.0015 (0.0005, 0.012) and 0.017 (0.005, 0.044), respectively (p?=?0.005).The results indicate that the constrained ABA algorithm can accurately align prone breast FDG-PET images acquired at different time points while keeping the tumor from being substantially compressed or distorted.NCT0047460418F-fluorodeoxyglucose positron emission tomography (FDG-PET) can provide estimates of parameters related to the delivery, retention, and metabolism of glucose, and therefore has been proposed as a method of characterizing the response of tumors to treatment [1-4]. To assess treatment response, semi-quantitative parameters, such as the standard uptake value (SUV) which is derived from static images [5,6], or quantitative parameters which are derived from dynamic images, [7-9], are measured at d
Shielded transceiver RF coil array for simultaneous PET-MRI
Solis, E.;Tomasi, D.;Junnarkar, S.;Schlyer, D.;Vaska, P.;Woody, C.;Pratte, J-F.;O'Connor, P.;Rodriguez, A. O.;
Brazilian Journal of Physics , 2008, DOI: 10.1590/S0103-97332008000200013
Abstract: the complementary information provided by combined mri-pet modalities promises to facilitate metabolic investigations of complex physiological processes. we developed a radio frequency (rf) coil array that can operate in close proximity (2-mm radial distance) to a miniaturized pet camera insert for simultaneous pet-mri of a rat brain at high magnetic fields (4 tesla). all ferromagnetic components in the pet instrument were replaced with non-ferromagnetic components to minimize susceptibility artefacts in mri, and optical fibres were used to connect the electronics of the pet camera to the acquisition system located outside the mri scanner room. a passive electromagnetic shielding was developed to minimize the interference between the pet-electronics and mri rf coil array. mr images of water phantoms and "ex-vivo" rat brains were collected in two different conditions: with and without pet acquisition. similarly, pet data was acquired in two different conditions: with and without mri pulses (rf and gradients). the mr images showed good uniform sensitivity profiles for all cases and 66% decrease in snr for the shielded case. the pet and mri datasets demonstrated that the electromagnetic shielding successfully minimizes the rf interference between the instruments, minimizing mri artefacts and protecting the delicate components of the pet electronics from mri rf pulses.
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