scholarly journals MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3185
Author(s):  
Marko Panić ◽  
Dušan Jakovetić ◽  
Dejan Vukobratović ◽  
Vladimir Crnojević ◽  
Aleksandra Pižurica
Keyword(s):  
Random Field ◽  
Total Variation ◽  
Markov Random Field ◽  
Adaptive Estimation ◽  
A Priori ◽  
Reconstruction Algorithm ◽  
Original Data ◽  
Magnetic Resonance Images ◽  
Reconstruction Method ◽  
Markov Random

Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field.

scholarly journals Coherent Markov Random Field-Based Unreliable DSM Areas Segmentation and Hierarchical Adaptive Surface Fitting for InSAR DEM Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1414 ◽  
Author(s):  
Qian Qian ◽  
Bingnan Wang ◽  
Xiaoning Hu ◽  
Maosheng Xiang
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Surface Fitting ◽  
Grid Size ◽  
Surface Model ◽  
Reconstruction Method ◽  
Markov Random ◽  
Distance Weighted ◽  
Natural Terrain ◽  
Set Up

A digital elevation model (DEM) can be obtained by removing ground objects, such as buildings, in a digital surface model (DSM) generated by the interferometric synthetic aperture radar (InSAR) system. However, the imaging mechanism will cause unreliable DSM areas such as layover and shadow in the building areas, which seriously affect the elevation accuracy of the DEM generated from the DSM. Driven by above problem, this paper proposed a novel DEM reconstruction method. Coherent Markov random field (CMRF) was first used to segment unreliable DSM areas. With the help of coherence coefficients and residue information provided by the InSAR system, CMRF has shown better segmentation results than traditional traditional Markov random field (MRF) which only use fixed parameters to determine the neighborhood energy. Based on segmentation results, the hierarchical adaptive surface fitting (with gradually changing the grid size and adaptive threshold) was set up to locate the non-ground points. The adaptive surface fitting was superior to the surface fitting-based method with fixed grid size and threshold of height differences. Finally, interpolation based on an inverse distance weighted (IDW) algorithm combining coherence coefficient was performed to reconstruct a DEM. The airborne InSAR data from the Institute of Electronics, Chinese Academy of Sciences has been researched, and the experimental results show that our method can filter out buildings and identify natural terrain effectively while retaining most of the terrain features.

Constrained Compound Markov Random Field Model with Graduated Penalty Function for Color Image Segmentation

2011 ◽  
Vol 403-408 ◽  
pp. 3438-3445
Author(s):  
Sucheta Panda ◽  
P.K. Nanda
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Markov Random Field ◽  
Color Image ◽  
A Priori ◽  
Color Space ◽  
Color Image Segmentation ◽  
Misclassification Error ◽  
Model Parameters ◽  
Markov Random

In this paper, an unsupervised color image segmentation scheme has been proposed for preserving strong and weak edges as well. A Constrained Compound Markov Random Field (MRF) has been proposed as the a priori model for the color labels. We have used Ohta (I1, I2, I3) color model and a controlled correlation of the color space has been accomplished by the proposed compound MRF model. The Constrained Compound MRF (CCMRF) is found to possess the unifying property of modeling scenes as well as color textures. In unsupervised scheme, the associated model parameters and the image labels are estimated recursively. The model parameters are the Maximum Conditional Pseudo Likelihood (MCPL) estimates and the labels are the Maximum a Posteriori (MAP) estimates. The performance of the proposed scheme has been compared with that of Yu’s method and has been found to exhibit improved performance in the context of misclassification error.

scholarly journals Reference Fields Analysis of a Markov Random Field Model to Improve Image Segmentation

2010 ◽  
Vol 8 (02) ◽  
Author(s):  
E. D. López-Espinoza ◽  
L. Altamirano-Robles
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Markov Random Field ◽  
Statistical Significance ◽  
A Priori ◽  
Computational Time ◽  
Internal Reference ◽  
Markov Random ◽  
Segmentation Quality ◽  
Reference Fields

In Markov random field (MRF) models, parameters such as internal and external reference fields are used. In this paper, the influence of these parameters in the segmentation quality is analyzed, and it is shown that, for image segmentation, a MRF model with a priori energy function defined by means of non-homogeneous internal and external field has better segmentation quality than a MRF model defined only by a homogeneous internal reference field. An analysis of the MRF models in terms of segmentation quality, computational time and tests of statistical significance is done. Significance tests showed that the segmentations obtained with MRF model defined by means of non-homogeneous reference fields are significant at levels of 85% and 75%.

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