Markov Random Field Approach Research Articles

Image-to-Height Domain Translation for Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) intensity statistics are dependent upon the sensing geometry at the time of capture. Estimating bathymetry from acoustic surveys is challenging. While several methods have been proposed to estimate seabed relief via intensity, we develop the first large-scale study that relies on deep learning models. In this work, we pose bathymetric estimation from SAS surveys as a domain translation problem of translating intensity to height. Since no dataset of coregistered seabed relief maps and sonar imagery previously existed to learn this domain translation, we produce the first large simulated dataset containing coregistered pairs of seabed relief and intensity maps from two unique sonar data simulation techniques. We apply four types of models, with varying complexity, to translate intensity imagery to seabed relief: a shape-from-shading (SFS) approach, a Gaussian Markov random field (GMRF) approach, a conditional Generative Adversarial Network (cGAN), and UNet architectures. Each model is applied to datasets containing sand ripples, rocky, mixed, and flat sea bottoms. Methods are compared in reference to the coregistered simulated datasets using L1 error. Additionally, we provide results on simulated and real SAS imagery. Our results indicate that the proposed UNet architectures outperform an SFS, a GMRF, and a pix2pix cGAN model.

A Markov random field approach for CT image lung classification using image processing

The performance of computed tomography lung classification using image processing and Markov Random Field was investigated in this study. For lung classification, the process must first be going through lung segmentation process. Lung segmentation is important as an initial process before lung cancer segmentation and analysis. Image processing was employed to the input image. We propose multilevel thresholding and Markov Random Field to improve the segmentation process. Three setting for Markov Random Field was used for segmentation process that is Iterated Condition Mode, Metropolis algorithm and Gibbs sampler. Then, the process of classifying lung will proceed. The output from the experiments were analysed and compared to get the best performance. The results revealed that for CT image lung classification, Markov Random Field using Metropolis algorithm gives the best results. In view of the result obtained, the average accuracy is 94.75% while the average sensitivity and specificity are 76.34% and 99.80%. The output from this study can be implemented in lung cancer analysis research and computer aided diagnosis development.

Defining and analysis of multimorbidity pattern of diseases using Markov random field approach: a comparative analysis

Defining and analysis of multimorbidity pattern of diseases using Markov random field approach: a comparative analysis

Markov Random Field based microstructure reconstruction using the principal image moments

The present work addresses the microstructure reconstruction of forged and additively manufactured materials using Markov Random Field (MRF) approach and the principal of image moments. The MRF based reconstruction is performed for the experimental samples to predict the spatial evolution of the microstructures on a larger scale. To achieve a high-fidelity statistically-equivalent representation for the original image, the synthesized samples are assessed according to their global and local level features. The global parameters measure the averaged properties of the microstructure image. In particular, they are defined as the distance to the image centroid and the eigenvalues of the covariance matrix of the normalized central moments. The local features are associated with the grain-level properties, including grain size and shape which are computed using the image moment values. With our presented approach, we not only demonstrate a computational framework for the spatial reconstruction of microstructures but also guarantee the statistical equivalency of the synthesized samples to the original microstructure images. • The microstructure evolution in large length-scales is predicted using the Markov Random Field approach. • The image moments are used to compare the synthesized microstructures with experimental samples in global and local levels. • The first Hu moment is observed to be dominant over other moments for capturing the grain shapes. • The uncertainty of the experimental images is found to be more effective in the local-level.

A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis

Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed. Among these methods, random field models play an indispensable role in improving the analysis performance. In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models. Firstly, we introduce the background of two random fields and pathology images. Secondly, we summarize the basic mathematical knowledge of MRFs and CRFs from modelling to optimization. Then, a thorough review of the recent research on the MRFs and CRFs of pathology images analysis is presented. Finally, we investigate the popular methodologies in the related works and discuss the method migration among CAD field.

Abundance data applied to a novel model invertebrate host shed new light on parasite community assembly in nature.

Understanding how environmental drivers influence the assembly of parasite communities, in addition to how parasites may interact at an infracommunity level, are fundamental requirements for the study of parasite ecology. Knowledge of how parasite communities are assembled will help to predict the risk of parasitism for hosts, and model how parasite communities may change under variable conditions. However, studies frequently rely on presence-absence data and examine multiple host species or sites, metrics which may be too coarse to characterise nuanced within-host patterns. We utilised a novel host system, the freshwater mussel Anodonta anatina, to investigate the drivers of community structure and explore parasite interactions. In addition, we aimed to highlight consistencies and inconsistencies between PA and abundance data. Our analysis incorporated 14 parasite taxa and 720 replicate infracommunities. Using Redundancy Analysis, a joint species distribution model and a Markov random field approach, we modelled the impact of both host-level and environment-level characteristics on parasite structure, as well as parasite-parasite correlations after accounting for all other factors. This approach was repeated for both the presence and abundance of all parasites. We demonstrated that the regional species pool, individual host characteristics (mussel length and gravidity) and predicted parasite-parasite interactions are all important but to varying degrees across parasite species, suggesting that applying generalities to parasite community construction is too simplistic. Furthermore, we showed that PA data fail to capture important density-dependent effects of parasite load for parasites with high abundance, and in general performs poorly for high-intensity parasites. Host and parasite traits, as well as broader environmental factors, all contribute to parasite community structure, emphasising that an integrated approach is required to study community assembly. However, care must be taken with the data used to infer patterns, as presence-absence data may lead to incorrect ecological inference.

Diagnosis of Distant Metastasis of Lung Cancer Using Anarchic Society Optimization with Support Vector Machine

The most widely recognized threatening tumours is the lung cancer one amongst the most preeminent disease and mortality which prompts significant risk to individuals’ wellbeing and life. Propelled lung malignant growth is probably prompt to produce comparing side effects in patients with extraordinary torment and life-threatening. Computed Tomography (CT) is one of the efficient solutions to investigate the distant metastasis plausibility for which system with computer aided diagnosis is desirable. So to diagnose distant metastasis of cancer in lung, the past framework is accomplished by Support Vector Machine (SVM) based classification for manual segmentation and the precision achieved is 89.09% for the classification. To enumerate large data sets, manual segmentation is time consuming, tedious and labour-intensive and hence achieving feasibility is not that easy. The desired prediction accuracy is attained by the techniques namely efficient segmentation method and feature selection methods. The proposed system utilizes Improved Markov Random Field (MRF) with SVM based classification, thereby improving the system performance. In this research work, the input is nothing but the CT lung images and adaptive median filtering is utilized for preprocessing technique. Improved MRF approach is one of the solutions to segment the pre-processed image after which CT images are used to extricate the clinical features and radiomic features. Anarchic Society Optimization (ASO) algorithm provides assistance for choosing the optimal features which in turn progresses the classification accuracy. In view of the selected feature, SVM classifier is utilized for cancer classification. The test outcome proves that the projected framework achieves improved performance compared to the existing frameworks parameters like correctness, accuracy and review.

MARKOV RASTGELE ALANLAR YÖNTEMİ KULLANILARAK HİTİT HUWASİ KUTSAL SAHASININ GÖRÜNTÜLENMESİ

in this paper, regional and residual anomaly separation is achieved using Markov Random Field (MRF) approach. MRF needs no training and uses the benefıl of neighbourhood of pixels of the input anomaly. It is an update stoachastic model. MRF has applied to Sivas city, Altınyayla- Kusaklı Hitit civilisation and satisfactory results are obtained.

Hybrid Seismic Inversion Based on Multi-Order Anisotropic Markov Random Field

The prestack seismic inversion effectively transforms reflection data into elastic parameters attributed to rock properties, which has enabled us to explore subsurface in detail. Because the forwarding operator is nonlinear and the observed data are usually insufficient, the inverse problem suffers from ill-posedness. Regularization has proven to be an effective technique making the problem well-posed by adding extra constraints to the estimated model. In particular, the anisotropic Markov random field (AMRF) approach can formulate the prior constraints stabilizing inversion procedure and taking special care of the geological boundaries. However, the standard AMRF only deals with the first-order neighborhood and its capability of revealing geological details is thereby limited. In addition, the auxiliary model, which significantly affects the performance of the AMRFs on correcting anisotropic gradients, is difficult to determine because reliable prior information is usually absent. This articlepresents a hybrid seismic inversion method for estimating the elastic parameters, which is featured by the multi-order AMRF (MAMRF) neighborhoods. In specific, the poststack inverted acoustic impedance result is used as the auxiliary model for the subsequent prestack seismic inversion; the MAMRF-based prior constraint incorporates low- and high-order AMRF neighborhoods during the inversion procedure, whose capability of correcting anisotropic gradients is achieved by the MAMRF coefficients and is enhanced by order-depended and statistically estimated diffusion coefficients. The proposed inversion method is verified by a synthetic test followed by application to inverting field data. Compared with the standard MRF and (first-order) AMRF approaches, the proposed inversion method is applicable for revealing detailed subsurface models, especially under complex geological conditions.

A latent discrete Markov random field approach to identifying and classifying historical forest communities based on spatial multivariate tree species counts

The Wisconsin Public Land Survey database describes historical forest composition at high spatial resolution and is of interest in ecological studies of forest composition in Wisconsin just prior to significant Euro-American settlement. For such studies it is useful to identify recurring subpopulations of tree species known as communities, but standard clustering approaches for subpopulation identification do not account for dependence between spatially nearby observations. Here, we develop and fit a latent discrete Markov random field model for the purpose of identifying and classifying historical forest communities based on spatially referenced multivariate tree species counts across Wisconsin. We show empirically for the actual dataset and through simulation that our latent Markov random field modeling approach improves prediction and parameter estimation performance. For model fitting we introduce a new stochastic approximation algorithm which enables computationally efficient estimation and classification of large amounts of spatial multivariate count data.

State‐of‐art analysis of image denoising methods using convolutional neural networks

Convolutional neural networks (CNNs) are deep neural networks that can be trained on large databases and show outstanding performance on object classification, segmentation, image denoising etc. In the past few years, several image denoising techniques have been developed to improve the quality of an image. The CNN based image denoising models have shown improvement in denoising performance as compared to non-CNN methods like block-matching and three-dimensional (3D) filtering, contemporary wavelet and Markov random field approaches etc. which had remained state-of-the-art for years. This study provides a comprehensive study of state-of-the-art image denoising methods using CNN. The literature associated with different CNNs used for image restoration like residual learning based models (DnCNN-S, DnCNN-B, IDCNN), non-locality reinforced (NN3D), fast and flexible network (FFDNet), deep shrinkage CNN (SCNN), a model for mixed noise reduction, denoising prior driven network (PDNN) are reviewed. DnCNN-S and PDNN remove Gaussian noise of fixed level, whereas DnCNN-B, IDCNN, NN3D and SCNN are used for blind Gaussian denoising. FFDNet is used for spatially variant Gaussian noise. The performance of these CNN models is analysed on BSD-68 and Set-12 datasets. PDNN shows the best result in terms of PSNR for both BSD-68 and Set-12 datasets.

A New Multiple Information Fusion Approach for Astronomical Navigation using Markov Random Field algorithm

Autonomous navigation is one of the key technologies to ensure the implementation of deep space exploration mission. Astronomical angle measurement information and stellar spectral velocity measurement information are usually used to estimate the real-time navigation state. According to the characteristics and requirements of deep space navigation, this paper proposes a novel method for information fusing using Markov Random Field approach, which integrates the navigation information including angle measurement, velocity measurement and combination of angle and velocity measurement, to realize continuous autonomous, real-time and high-precision navigation of deep space exploration. Considering the background of Mars exploration engineering mission, the observability and precision of the navigation method are analysed. The simulation results show that the method based on Markov Random Field can greatly improve the observability of the navigation system, effectively suppress the influence of measurement error, further improve the accuracy of navigation estimation, enhance the reliability of the navigation system, and provide a new technical approach to realize the high precision autonomous navigation of deep space exploration.

An Improved Anisotropic Markov Random Field Approach for Prestack Seismic Inversion

This letter presents an improved anisotropic Markov random field (IAMRF) approach for prestack seismic inversion. Instead of using multiple potential functions or adding extra weights on clique potentials as done in existing AMRF approaches, IAMRF removes the effects of anisotropic gradients in subsurface models by means of scaling parameters, which directly tune the model gradients right above. In particular, the scaling parameters are not only directionally varied at certain iterations but also updated by a statistical method at every iteration throughout the inversion, thereby generating more accurate and better edge-preserving inverted results. The prestack seismic inversion based on the IAMRF prior constraints is demonstrated on a field data example, which presents encouraging results.

Reservoir Lithology Determination by Hidden Markov Random Fields Based on a Gaussian Mixture Model

In this paper, geological prior information is incorporated in the classification of reservoir lithologies after the adoption of Markov random fields (MRFs). The prediction of hidden lithologies is based on measured observations, such as seismic inversion results, which are associated with the latent categorical variables, based on the assumption of Gaussian distributions. Compared with other statistical methods, such as the Gaussian mixture model or $k$ -Means, which do not take spatial relationships into account, the hidden MRFs approach can connect the same or similar lithologies horizontally while ensuring a geologically reasonable vertical ordering. It is, therefore, able to exclude randomly appearing lithologies caused by errors in the inversion. The prior information consists of a Gibbs distribution function and transition probability matrices. The Gibbs distribution connects the same or similar lithologies internally, which does not need a geological definition from the outside. The transition matrices provide preferential transitions between different lithologies, and an estimation of them implicitly depends on the depositional environments and juxtaposition rules between different lithologies. Analog cross sections from the subsurface or outcrop studies can contribute to the construction of these matrices by a simple counting procedure.

Precise Markov random field model-based phase unwrapping method for airborne interferometric synthetic aperture radar imaging

Markovian model approach with graph cuts is effectively applied for phase unwrapping (PU) in interferometric synthetic aperture radar (InSAR) domain particularly for data with big noises and discontinuities. The first-order prior model in Markov random field (MRF) is widely used in Markovian model approach, which is able to solve PU problem in homogeneous scenarios. However, the first-order MRF method cannot interpret local details well in abundant patterns of interferograms under the requirement of high accuracy due to the limitation of first-order prior information. Hence, the conventional first-order model is no longer applicable in this situation. In terms of this problem, the shortcoming of the first-order model is analyzed, and a precise second-order MRF approach is proposed in this paper. Furthermore, a graph cuts-based optimization PU algorithm is designed under a high-order MRF mode, which improves the PU accuracy in detail areas. Without apparent increase of computational complexity, the proposed method is more robust compared with the conventional first-order MRF-based methods. A set of experimental results on both simulation and real measured millimeter-wave InSAR data illustrate the effectiveness of the proposal.

A Network-Specific Markov Random Field Approach to Community Detection

Markov Random Field (MRF) is a powerful framework for developing probabilistic models of complex problems. MRF models possess rich structures to represent properties and constraints of a problem. It has been successful on many application problems, particularly those of computer vision and image processing, where data are structured, e.g., pixels are organized on grids. The problem of identifying communities in networks, which is essential for network analysis, is in principle analogous to finding objects in images. It is surprising that MRF has not yet been explored for network community detection. It is challenging to apply MRF to network analysis problems where data are organized on graphs with irregular structures. Here we present a network-specific MRF approach to community detection. The new method effectively encodes the structural properties of an irregular network in an energy function (the core of an MRF model) so that the minimization of the function gives rise to the best community structures. We analyzed the new MRF-based method on several synthetic benchmarks and real-world networks, showing its superior performance over the state-of-the-art methods for community identification.

Interferometric synthetic aperture radar phase unwrapping based on sparse Markov random fields by graph cuts

Phase unwrapping (PU) is one of the key processes in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) data. It is known that two-dimensional (2-D) PU problems can be formulated as maximum a posteriori estimation of Markov random fields (MRFs). However, considering that the traditional MRF algorithm is usually defined on a rectangular grid, it fails easily if large parts of the wrapped data are dominated by noise caused by large low-coherence area or rapid-topography variation. A PU solution based on sparse MRF is presented to extend the traditional MRF algorithm to deal with sparse data, which allows the unwrapping of InSAR data dominated by high phase noise. To speed up the graph cuts algorithm for sparse MRF, we designed dual elementary graphs and merged them to obtain the Delaunay triangle graph, which is used to minimize the energy function efficiently. The experiments on simulated and real data, compared with other existing algorithms, both confirm the effectiveness of the proposed MRF approach, which suffers less from decorrelation effects caused by large low-coherence area or rapid-topography variation.

Learning environmental fields with micro underwater vehicles: a path integral—Gaussian Markov random field approach

Autonomous underwater vehicles (AUVs) are advancing the state of the art in numerous scientific and commercial applications. The current surge in micro electronics enables the development of small micro AUVs ( $$\mu $$ AUVs) which are expected to gain increasing popularity in industrial applications such as monitoring of liquid-based processes. This paper presents an information theoretic approach for exploration and monitoring of liquid containing tanks with $$\mu $$ AUVs. The controller is based on ideas from path integral control and inference with Gaussian Markov random fields (GMRFs). Both parts are combined in a receding horizon scheme to the PI-GMRF controller. The control problem is formulated within the stochastic optimal control domain and a solution is stated as a path integral. In order to close the control theoretic loop each $$\mu $$ AUV maintains a belief representation of the environment expressed with GMRFs which allows reasoning by computing posterior distributions conditioned on measurements. Each $$\mu $$ AUV has its own controller instance and the system is decentral. Only the exchange of measurements and intended control inputs of each $$\mu $$ AUV is required through the communication link. The approach is validated in simulations for an advection–diffusion scenario and benchmarked against random walk, which it outperforms.

A fast source-oriented image clustering method for digital forensics

We present in this paper an algorithm that is capable of clustering images taken by an unknown number of unknown digital cameras into groups, such that each contains only images taken by the same source camera. It first extracts a sensor pattern noise (SPN) from each image, which serves as the fingerprint of the camera that has taken the image. The image clustering is performed based on the pairwise correlations between camera fingerprints extracted from images. During this process, each SPN is treated as a random variable and a Markov random field (MRF) approach is employed to iteratively assign a class label to each SPN (i.e., random variable). The clustering process requires no a priori knowledge about the dataset from the user. A concise yet effective cost function is formulated to allow different “neighbors” different voting power in determining the class label of the image in question depending on their similarities. Comparative experiments were carried out on the Dresden image database to demonstrate the advantages of the proposed clustering algorithm.

Graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture.

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/.