scholarly journals Multicomponent MR Image Denoising

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
José V. Manjón ◽  
Neil A. Thacker ◽  
Juan J. Lull ◽  
Gracian Garcia-Martí ◽  
Luís Martí-Bonmatí ◽  
...  
Keyword(s):  
Random Noise ◽  
Principal Component ◽  
Magnetic Resonance Images ◽  
Image Resolution ◽  
Single Image ◽  
Mr Images ◽  
Mr Image ◽  
Automatic Feature Extraction ◽  
Improved Performance ◽  
Multicomponent Nature

Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

scholarly journals The Effect of Labeled/Unlabeled Prior Information for Masseter Segmentation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yousef Rezaei Tabar ◽  
Ilkay Ulusoy
Keyword(s):  
Prior Information ◽  
Automatic Segmentation ◽  
Magnetic Resonance Images ◽  
Target Tissue ◽  
Mr Images ◽  
Mr Image ◽  
Seed Point ◽  
New Approach ◽  
Segmentation Methods ◽  
The Common

Several segmentation methods are implemented and applied to segment the facial masseter tissue from magnetic resonance images. The common idea for all methods is to take advantage of prior information from different MR images belonging to different individuals in segmentation of a test MR image. Standard atlas-based segmentation methods and probabilistic segmentation methods based on Markov random field use labeled prior information. In this study, a new approach is also proposed where unlabeled prior information from a set of MR images is used to segment masseter tissue in a probabilistic framework. The proposed method uses only a seed point that indicates the target tissue and performs automatic segmentation for the selected tissue without using labeled training set. The segmentation results of all methods are validated and compared where the influences of labeled or unlabeled prior information and initialization are discussed particularly. It is shown that if appropriate modeling is done, there is no need for labeled prior information. The best accuracy is obtained by the proposed approach where unlabeled prior information is used.

scholarly journals Optimized Resolution-Oriented Many-to-One Intensity Standardization Method for Magnetic Resonance Images

2019 ◽  
Vol 9 (24) ◽  
pp. 5531
Author(s):  
Yuan Gao ◽  
Yuanyuan Wang ◽  
Jinhua Yu
Keyword(s):  
Magnetic Resonance ◽  
Visual Information ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Image Resolution ◽  
Mr Images ◽  
Computer Aided ◽  
Aided Diagnosis

With the development of big data, Radiomics and deep-learning methods based on magnetic resonance (MR) images, it is necessary to conduct large databases containing MR images from multiple centers. Having huge intensity distribution differences among images reduced or even eliminated, robust computer-aided diagnosis models could be established. Therefore, an optimized intensity standardization model is proposed. The network structure, loss function, and data input strategy were optimized to better avoid the image resolution loss during transformation. The experimental dataset was obtained from five MR scanners located in four hospitals and was divided into nine groups based on the imaging parameters, during which 9152 MR images from 499 participants were collected. Experiments show the superiority of the proposed method to the previously proposed unified model in resolution metrics including the peak signal-to-noise ratio, structural similarity, visual information fidelity, universal quality index, and image fidelity criterion. Another experiment further shows the advantage of the proposed method in increasing the effectiveness of following computer-aided diagnosis models by better preservation of MR image details. Moreover, the advantage over conventional standardization methods are also shown. Thus, MR images from different centers can be standardized using the proposed method, which will facilitate numerous data-driven medical imaging studies.

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

Polarization analysis of high-frequency, three-component seismic data

1991 ◽  
Vol 81 (2) ◽  
pp. 622-642
Author(s):  
K. Bataille ◽  
J. M. Chiu
Keyword(s):  
High Frequency ◽  
Time Windows ◽  
Random Noise ◽  
Polarization State ◽  
Principal Component ◽  
Small Time ◽  
Body Waves ◽  
Local Data ◽  
Principal Component Approach ◽  
Component Approach

Abstract We present a method to determine the polarization of body waves from three-component, high-frequency data and examples of its application. The method is based on the principal component approach. One advantage of this approach is that the polarization state can be determined for small time windows compared with the predominant period of the wave. This is particularly useful for identifying converted waves within the crust. The stability of the result is analyzed with synthetic cases by adding simultaneous arrivals from waves and random noise. The method works well with both synthetic and local data in the detection of the polarization of the wave by separating arrivals from different directions. From the local data, some seismic phases related to crustal conversions are observed that require strong lateral variations.

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

2019 ◽  
Vol 17 (01) ◽  
pp. 1950003
Author(s):  
Apurba Roy ◽  
Santi P. Maity
Keyword(s):  
Image Reconstruction ◽  
Linear Prediction ◽  
Random Noise ◽  
Curvelet Transform ◽  
Conditional Entropy ◽  
Mr Images ◽  
Recursive Filtering ◽  
Entropy Maximization ◽  
Residual Noise ◽  
Incomplete Measurement

In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works.

scholarly journals Principal Component Analysis of Dynamic Relative Displacement Fields Estimated from MR Images

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e22063 ◽  
Author(s):  
Teresa M. Abney ◽  
Yuan Feng ◽  
Robert Pless ◽  
Ruth J. Okamoto ◽  
Guy M. Genin ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Relative Displacement ◽  
Mr Images ◽  
Displacement Fields

scholarly journals Super-resolution imaging of negative-refractive graded-index photonic crystal flat lens

2021 ◽  
Author(s):  
Binming Liang ◽  
Xiao Huang ◽  
Jihong Zheng
Keyword(s):  
Photonic Crystal ◽  
Imaging System ◽  
Super Resolution ◽  
Point Sources ◽  
Image Resolution ◽  
Multiple Point ◽  
Single Image ◽  
Graded Index ◽  
Resolution Imaging ◽  
Flat Lens

Abstract Photonic crystal (PC) not only breaks through the diffraction limit of traditional lenses but also can realize super-resolution imaging. Improving the resolution is the key task of PC imaging. The main work of this paper is to use a graded-index Photonic crystal (GPC) flat lens to improve the image resolution. An air-hole type two-dimensional (2D) GPC structure based on silicon medium is proposed in this paper. Numerical simulations through RSoft reveal that when the medium in the imaging area is air, the full width at half maximum (FWHM) value of a single image reaches 0.362λ. According to the Rayleigh criterion, the images of two point sources 0.57λ apart can also be distinguished. In the imaging system composed of cedar oil and GPC flat lens, the FWHM value of a single image reaches 0.34λ. In addition, the images of multiple point sources 0.49λ apart can still be distinguished.

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