Conventional Markov Random Field Research Articles

Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images

Breast cancer has emerged as the most life-threatening disease among women around the world. Early detection and treatment of breast cancer are thought to reduce the need for surgery and boost the survival rate. The Magnetic Resonance Imaging (MRI) segmentation techniques for breast cancer diagnosis are investigated in this article. Kapur’s entropy-based multilevel thresholding is used in this study to determine optimal values for breast DCE-MRI lesion segmentation using Gorilla Troops Optimization (GTO). An improved GTO, is developed by incorporating Rotational opposition based-learning (RBL) into GTO called (GTORBL) and applied it to the same problem. The proposed approaches are tested on 20 patients’ T2 Weighted Sagittal (T2 WS) DCE-MRI 100 slices. The proposed approaches are compared with Tunicate Swarm Algorithm (TSA), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Multi-verse Optimization (MVO), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF). The Dice Similarity Coefficient (DSC), sensitivity, and accuracy of the proposed GTO-based approach is achieved 87.04%, 90.96%, and 98.13% respectively. Another proposed GTORBL-based segmentation method achieves accuracy values of 99.31% , sensitivity of 95.45% , and DSC of 91.54%. The one-way ANOVA test followed by Tukey HSD and Wilcoxon Signed Rank Test are used to examine the results. Furthermore, Multi-Criteria Decision Making is used to evaluate overall performance focused on sensitivity, accuracy, false-positive rate, precision, specificity, F_1-score, Geometric-Mean, and DSC. According to both quantitative and qualitative findings, the proposed strategies outperform other compared methodologies.

Breast DCE-MRI segmentation for lesion detection using Chimp Optimization Algorithm

The high prevalence of breast cancer in women has increased dramatically in recent times. Physician’s knowledge in breast cancer diagnosis and detection using computerized algorithms for extraction and segmentation of features can help. Image segmentation is a critical component of image analysis that has a direct impact on the quality of the results. This article presents Kapur’s entropy-based multilevel thresholding using Chimp Optimization Algorithm (ChOA) to estimate optimal values for the lesion segmentation of breast DCE-MRI. An improved ChOA is also developed by incorporating Opposition based-learning (OBL) in it, termed as ChOAOBL, and applied to solve the same problem. The proposed methods are evaluated using 200 Sagittal T2-Weighted fat-suppressed DCE-MRI images of 40 patients. The proposed methods are compared with Improved ChOA (IChOA), Particle Swarm Optimization (PSO), Multi-verse Optimizer (MVO), Slime Mould Algorithm (SMA), Arithmetic Optimization Algorithm (AOA), Tunicate Swarm Algorithm (TSA), Multilevel Otsu Threshold (MLOT), Conventional Markov Random Field (CMRF), Hidden Markov Random Field (HMRF), and Improved Markov Random Field (IMRF). The high sensitivity, accuracy, and Dice Efficient Coefficient (DSC) level of the proposed ChOA-based method are achieved at 90.75%, 98.24%, and 87.09% respectively. The accuracy value of 99.02%, sensitivity 95.73%, and DSC 93.25% are achieved using another proposed ChOAOBL-based segmentation method. The results are analyzed using a one-way ANOVA test followed by Tukey HSD, and Wilcoxon Signed Rank Test. We have also analyzed the overall performance using Multi-Criteria Decision Making based on accuracy, precision, specificity, F-measure, sensitivity, false-positive rate, Geometric-Mean (G-mean), and DSC. The proposed methods outperform other compared methods, according to both quantitative and qualitative outcomes.

Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization

In recent years, the high prevalence of breast cancer in women has risen dramatically. Therefore, segmentation of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a necessary task to assist the radiologist inaccurate diagnosis and detection of breast cancer in breast DCE-MRI. For image segmentation, thresholding is a simple and effective method. In breast DCE-MRI analysis for lesion detection and segmentation, radiologists agree that optimization via multi-level thresholding technique is important to differentiate breast lesions from dynamic DCE-MRI. In this paper, multi-level thresholding using Student Psychology-Based Optimizer (SPBO) is proposed to segment the breast DCE-MR images for lesion detection. First, MR images are denoised using the anisotropic diffusion filter and then, Intensity Inhomogeneities (IIHs) are corrected in the preprocessing step. The preprocessed MR images are segmented using the SPBO algorithm. Finally, the lesions are extracted from the segmented images and localized in the original MR images. The proposed method is applied on 300 Sagittal T2-Weighted DCE-MRI slices of 50 patients, histologically proven, and analyzed. The proposed method is compared with algorithms such as Particle Swarm Optimizer (PSO), Dragonfly Optimization (DA), Slime Mould Optimization (SMA), Multi-Verse Optimization (MVO), Grasshopper Optimization Algorithm (GOA), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF) methods. The high accuracy level of 99.44%, sensitivity 96.84%, and Dice Similarity Coefficient (DSC) 93.41% are achieved using the proposed automatic segmentation method. Both quantitative and qualitative results demonstrate that the proposed method performs better than the eight compared methods.

Higher-order potentials for video object segmentation in bilateral space

We propose an effective approach to make segmentation for objects in videos with an initial input of the object masks in a few frames of the source video. In this method, we cast the segmentation task as a Markov Random Field (MRF) labeling problem. Different from the conventional MRF models, our model uses an additional term of higher-order potential to better propagate the global consistency among frames. The higher-order potential presented in this paper is significant for the proposed method because of its capability to keep the long-range consistency during segmentation. In order to make the MRF energy minimized, we also introduce a smart skill that makes the intractable higher-order potential “invisible” during the optimization so that the problem can be solved simply by applying a standard graph cut algorithm. Besides, the entire process is operated in a bilateral space, where the labeling can be inferred efficiently on the vertices that are sampled regularly from the bilateral grid. The results of a comparison of our method with a number of recently developed methods show that it performs favorably against state-of-the-art algorithms on multiple benchmark data sets in view of accuracy and achieves a much faster runtime performance.

Low-Power Noise-Immune Nanoscale Circuit Design Using Coding-Based Partial MRF Method

Reliability is one of the major concerns for ultralow power circuit designs. Markov random field (MRF) techniques have been applied to logic circuits to resist random noise when operating under ultralow supply voltage or sub-threshold voltage. Although conventional MRF networks can be easily mapped onto simple logic circuits, it becomes difficult when the circuits are large and complex. In this paper, we present a general coding-based partial MRF (CPMRF) method for multi-logic operations in one basic unit, which is referred to as a CPMRF pair. A CPMRF pair saves circuit area by sharing a common MRF network. It also inherits noise immunity from the MRF theory while obtaining noise immunity from the coding structure as a combination of robust “1s” and “0s.” The resulting architectures become more cost effective than conventional ones. To validate the performance of our proof-of-concept design, we fabricated a carry-lookahead adder implemented by the proposed CPMRF pairs using IBM 130-nm CMOS technology. Measurement results indicate that the CPMRF CLA can achieve high noise tolerance with 20% improvement while occupying 37.7% less area and reducing power consumption by 93% compared with the master-and-slave MRF CLA design.

Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field

Markov random field (MRF) model is an effective tool for polarimetric synthetic aperture radar (PolSAR) image classification. However, due to the lack of suitable contextual information in conventional MRF methods, there is usually a contradiction between edge preservation and region homogeneity in the classification result. To preserve edge details and obtain homogeneous regions simultaneously, an adaptive MRF framework is proposed based on a polarimetric sketch map. The polarimetric sketch map can provide the edge positions and edge directions in detail, which can guide the selection of neighborhood structures. Specifically, the polarimetric sketch map is extracted to partition a PolSAR image into structural and nonstructural parts, and then adaptive neighborhoods are learned for two parts. For structural areas, geometric weighted neighborhood structures are constructed to preserve image details. For nonstructural areas, the maximum homogeneous regions are obtained to improve the region homogeneity. Experiments are taken on both the simulated and real PolSAR data, and the experimental results illustrate that the proposed method can obtain better performance on both region homogeneity and edge preservation than the state-of-the-art methods.

Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images

The segmentation of the fluid-associated region in the retina plays an important role in the treatment of retinal diseases, such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). The existing methods for the detection of the fluid region generally suffer from the limitation of the segmentation accuracy and the expensive time costs. To overcome these problems, in this paper, we propose an interactive segmentation method for the fluid-associated region in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) retinal imaging, in which only a few seeds in one SD-OCT slice are needed, after which the algorithm finishes the segmentation automatically. To improve the segmentation accuracy, the higher-order constraint is introduced into the conventional Markov random field (MRF) framework to impose the superpixel consistency. To maintain temporal coherence of the 3-D SD-OCT slices, the labeling information is propagated slice by slice via a proposed motion-estimation-based algorithm. The proposed higher-order-based energy function can be efficiently solved by the max flow algorithm on a specified graph with several auxiliary nodes. Experiments on 28 SD-OCT cubes demonstrate the competitiveness of the proposed method compared with the state-of-the-art methods.

Integrating Hierarchical Segmentation Maps With MRF Prior for Classification of Hyperspectral Images in a Bayesian Framework

This paper presents a new spectral-spatial classification method for hyperspectral (HS) images. The proposed method is based on integrating hierarchical segmentation results into Markov random field (MRF) spatial prior in the Bayesian framework. This work includes two main contributions. First, statistical region merging (SRM) segmentation algorithm is extended to a hierarchical version, HSRM. Second, a method for extracting a multilevel “fuzzy no-border/border” map from HSRM segmentation hierarchy is proposed, which are then exploited as weighting coefficients to modify the spatial prior of MRF-based multilevel logistic (MLL) model. The proposed method, named as MRF + HSRM, addresses the common problem of MRF-based methods, i.e., over-smoothing of classification result. Several experiments are conducted using real HS images to evaluate the performance of the proposed method in comparison with conventional MRF, and some state-of-the-art weighted MRF and object-based classifiers. To estimate the class conditional probability distribution in Bayesian framework, probabilistic support vector machine (SVM) and subspace multinomial logistic regression (MLRsub) classifiers are used. The experimental results demonstrate that the proposed method is able to generate more homogeneous regions similar to MRF-based methods, while preserving class boundaries as accurately as segmentation-based methods. The overhead computational burden of the proposed hierarchical segmentation stage is negligible considering the improvement it offers in classification results.

Semantic volume segmentation with iterative context integration for bio-medical image stacks

Automatic recognition of biological structures like membranes or synapses is important to analyze organic processes and to understand their functional behavior. To achieve this, volumetric images taken by electron microscopy or computer tomography have to be segmented into meaningful semantic regions. We are extending iterative context forests which were developed for 2D image data to image stack segmentation. In particular, our method is able to learn high-order dependencies and import contextual information, which often can not be learned by conventional Markov random field approaches usually used for this task. Our method is tested on very different and challenging medical and biological segmentation tasks.

Approximate Translational Building Blocks for Image Decomposition and Synthesis

We introduce approximate translational building blocks for unsupervised image decomposition. Such building blocks are frequently appearing copies of image patches that are mapped coherently under translations. We exploit the coherency assumption to find approximate building blocks in noisy and ambiguous image data, using a spectral embedding of re-occurrence patterns. We quantitatively evaluate our method on a large benchmark dataset and obtain clear improvements over state-of-the-art methods. We apply our method to texture synthesis by integrating building block constraints and their offset statistics into a conventional Markov random field model. A user study shows improved retargeting results even if the images are only partially described by a few classes of building blocks.

Improving the runtime of MRF based method for MRI brain segmentation

Image segmentation is one of the important parts in medical image analysis. Markov random field (MRF) is one of the successful methods for MRI image segmentation, but conventional MRF methods suffer from high computational cost. MRI images have high level of artifacts such as Partial Volume Effect (PVE), intensity non uniformity (INU) and other noises, so using global optimization methods like simulated annealing (SA) for optimization step is more appropriate than other local optimization methods such as Iterative Conditional Modes (ICM). On the other hand, these methods also has heavy computational burden and they are not appropriate for real time task. This paper uses a proper combination of clustering methods and MRF and proposes a preprocessing step for MRF method for decreasing the computational burden of MRF for segmentation. The results show that the preprocessing step increased the speed of segmentation algorithm by a factor of about 10 and have no large impact on the accuracy of segmentation. Moreover, different clustering methods can be used for the first step and estimation of the parameters. Therefore, using of powerful clustering methods can provide a better segmentation results.

Incorporating uncertanity into Markov random field classification with the combine use of optical and SAR images and aduptive fuzzy mean vector

Abstract. A Markov Random Field (MRF) model accounting for the classification uncertainty using multisource satellite images and an adaptive fuzzy class mean vector is proposed in this study. The work also highlights the initialization of the class values for an MRF based classification for synthetic aperture radar (SAR) images using optical data. The model uses the contextual information from the optical image pixels and the SAR pixel intensity with corresponding fuzzy grade of memberships respectively, in the classification mechanism. Sub pixel class fractions estimated using Singular Value Decomposition (SVD) from the optical image initializes the class arrangement for the MRF process. Pair-site interactions of the pixels are used to model the prior energy from the initial class arrangement. Fuzzy class mean vector from the SAR intensity pixels is calculated using Fuzzy C-means (FCM) partitioning. Conditional probability for each class was determined by a Gamma distribution for the SAR image. Simulated annealing (SA) to minimize the global energy was executed using a logarithmic and power-law combined annealing schedule. Proposed technique was tested using an Advanced Land Observation Satellite (ALOS) phased array type L-band SAR (PALSAR) and Advanced Visible and Near-Infrared Radiometer-2 (AVNIR-2) data set over a disaster effected urban region in Japan. Proposed method and the conventional MRF results were evaluated with neural network (NN) and support vector machine (SVM) based classifications. The results suggest the possible integration of an adaptive fuzzy class mean vector and multisource data is promising for imprecise class discrimination using a MRF based classification.

Adaptive stochastic minimization for measuring marine oil spill extent in synthetic aperture radar images

A binary segmentation scheme, based on the Markov random field theory, is pre- sented. In order to obtain a more integrated label field, the simulated annealing schedule is modi- fied for performing a joint conditional estimation of model parameters. To reach a finer detection, the pixel neighborhood system of the a priori model is continuously updated at each cycle of the optimization algorithm. Maximum a posteriori is the central criterion of these algorithms. The proposed processing scheme is applied to a sequence of Envisat/ ASAR images of the Deepwater Horizon disaster of the Gulf of Mexico in the spring of 2010. Initial oil spills statistical parameters are extracted by visual analysis, but they are updated during the minimization cycles. The proposed scheme, when compared with a conventional Markov random field one, provides a better detection of fine structures. In addition, facing the complex ocean phenomena reflected in the synthetic aperture radar images, the final label field results are extremely well defined. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10

Deriving adaptive MRF coefficients from previous normal‐dose CT scan for low‐dose image reconstruction via penalized weighted least‐squares minimization

Repeated computed tomography (CT) scans are required for some clinical applications such as image-guided interventions. To optimize radiation dose utility, a normal-dose scan is often first performed to set up reference, followed by a series of low-dose scans for intervention. One common strategy to achieve the low-dose scan is to lower the x-ray tube current and exposure time (mAs) or tube voltage (kVp) setting in the scanning protocol, but the resulted image quality by the conventional filtered back-projection (FBP) method may be severely degraded due to the excessive noise. Penalized weighted least-squares (PWLS) image reconstruction has shown the potential to significantly improve the image quality from low-mAs acquisitions, where the penalty plays an important role. In this work, the authors' explore an adaptive Markov random field (MRF)-based penalty term by utilizing previous normal-dose scan to improve the subsequent low-dose scans image reconstruction. In this work, the authors employ the widely-used quadratic-form MRF as the penalty model and explore a novel idea of using the previous normal-dose scan to obtain the MRF coefficients for adaptive reconstruction of the low-dose images. In the coefficients determination, the authors further explore another novel idea of using the normal-dose scan to obtain a scale map, which describes an optimal neighborhood for the coefficients determination such that a local uniform region has a small spread of frequency spectrum and, therefore, a small MRF window, and vice versa. The proposed penalty term is incorporated into the PWLS image reconstruction framework, and the low-dose images are reconstructed via the PWLS minimization. The presented adaptive MRF based PWLS algorithm was validated by physical phantom and patient data. The experimental results demonstrated that the presented algorithm is superior to the PWLS reconstruction using the conventional Gaussian MRF penalty or the edge-preserving Huber penalty and the conventional FBP method, in terms of image noise reduction and edge/detail/contrast preservation. This study demonstrated the feasibility and efficacy of the proposed scheme in utilizing previous normal-dose CT scan to improve the subsequent low-dose scans.

Subpixel Mapping Using Markov Random Field With Multiple Spectral Constraints From Subpixel Shifted Remote Sensing Images

Subpixel mapping (SPM) is a promising technique to increase the spatial resolution of land cover maps. Markov random field (MRF)-based SPM has the advantages of considering spatial and spectral constraints simultaneously. In the conventional MRF, only the spectral information of one observed coarse spatial resolution image is utilized, which limits the SPM accuracy. In this letter, supplementary information from subpixel shifted remote sensing images (SSRSI) is used with MRF to produce more accurate SPM results. That is, spectral information from SSRSI is incorporated into the likelihood energy function of MRF to provide multiple spectral constraints. Simulated and real images were tested with the subpixel/pixel spatial attraction model, Hopfield neural networks (HNNs), HNN with SSRSI, image interpolation then hard classification, conventional MRF, and proposed MRF with SSRSI based SPM methods. Results showed that the proposed method can generate the most accurate SPM results among these methods.

Computationally Tractable Stochastic Image Modeling Based on Symmetric Markov Mesh Random Fields

In this paper, the properties of a new class of causal Markov random fields, named symmetric Markov mesh random field, are initially discussed. It is shown that the symmetric Markov mesh random fields from the upper corners are equivalent to the symmetric Markov mesh random fields from the lower corners. Based on this new random field, a symmetric, corner-independent, and isotropic image model is then derived which incorporates the dependency of a pixel on all its neighbors. The introduced image model comprises the product of several local 1D density and 2D joint density functions of pixels in an image thus making it computationally tractable and practically feasible by allowing the use of histogram and joint histogram approximations to estimate the model parameters. An image restoration application is also presented to confirm the effectiveness of the model developed. The experimental results demonstrate that this new model provides an improved tool for image modeling purposes compared to the conventional Markov random field models.

An improved MRF-based change detection approach for multitemporal remote sensing imagery

In the task of multitemporal remote sensing image change detection, conventional Markov random field (MRF) based approaches consider contextual information between neighboring pixels to obtain the change map. However, these approaches often get erroneous results at discontinuities such as edges, ridges and valleys, since they assume that neighboring pixels tend to have the same label. To overcome this, an improved MRF based change detection approach for multitemporal remote sensing imagery is proposed. The method first finds edges in the difference image by using the line process. Then, the weights of MRF prior energy are adaptively adjusted by considering the gray level differences between neighboring pixels. A group of adaptive weighting functions are defined in the study, and their performances in the task of change detection are compared. Experimental results confirm the proposed approach.

IMPROVING MARKOV RANDOM FIELD BASED SUPER RESOLUTION MAPPING THROUGH FUZZY PARAMETER INTEGRATION

Abstract. The objective of this study was to improve the Markov Random Field (MRF) based Super Resolution Mapping (SRM) technique to account for the vague land-cover interpretations (class mixture and the intermediate conditions) in an urban area. The algorithm has been improved to integrate the fuzzy mean and fuzzy covariance measurements, to a MRF based SRM scheme to optimize the classification results. The technique was tested on a WORLDVIEW-2 data set, acquired over a highway construction area, in Colombo, Sri Lanka. Based on the visual interpretation of the image, three major land-cover types of this area were identified for the study; those were vegetation, soil and exposed grass and impervious surface with low medium and high albedo. The membership values for each pixel were determined from training samples through Spectral Angle Mapper (SAM) technique. The compulsory fuzzy mean and the covariance measurements were derived using these membership grades, and subsequently was applied in MRF based SRM technique. The primary reference data was generated using Maximum Likelihood Classification (MLC) performed on the same data which was resampled to 1m resolution. The scale factor was set to be (S) = 2, to generate SRM of 1m resolution. The smoothening parameter (λ) which balances the prior and likelihood energy terms were tested in the range from 0.3 to 0.9. SRM were generated using fuzzy MRF and the conventional MRF models respectively. Results suggest that the fuzzy integrated model has improved the results with an overall accuracy of 85.60% and kappa value of 0.78 between the optimal results and the reference data, while in the conventional case it was 77.81% of overall accuracy with kappa being 0.65. Among the two MRF models, fuzzy parameter integrated model shows the highest agreement with class fractions from the reference image with a smallest average _MAE (MAE, Mean Absolute Error) of 0.03.

Bilateral Markov mesh random field and its application to image restoration

This paper introduces bilateral Markov mesh random field to overcome the shortcomings of the conventional Markov random fields in image modeling. These shortcomings consist of (a) the computational intractability of such fields when expressing the image probability function in the form of the Gibbs distribution function, and (b) the formulation of the image probability function via the product of low-dimensional densities at the expense of obtaining non-symmetrical image models. The properties of bilateral Markov mesh random field are presented and used to derive an image model to address the above shortcomings. As an application, a framework for image restoration is then provided. Restoration results based on this new bilateral Markov mesh random field are compared to the conventional fields to demonstrate its effectiveness.

Classification of high-spatial resolution imagery based on distance-weighted Markov random field with an improved iterated conditional mode method

In this article, we propose a Distance-Weighted Markov Random Field (DwMRF) for classification of high–spatial resolution imagery. The proposed DwMRF integrates the spectral and spatial information of the image, and better coordinates the interaction between neighbours and the central pixels than the conventional Equal-weighted MRF (EwMRF). In addition, we propose a Serial Iterated Conditional Mode (SICM) method for the solution of the Markov Random Field (MRF) model. Experiments are conducted on three data sets: HYDICE data of the Mall in Washington, DC, HYMAP data of Purdue University and QuickBird data of Beijing. We compare the proposed DwMRF approach with other methods: the EwMRF, Maximum Likelihood Classification (MLC) and a multiresolution segmentation (Fractal Net Evolution Approach (FNEA)) method. Experiments show the DwMRF is robust and outperforms the other methods; furthermore, the proposed SICM method converges more rapidly than conventional Iterated Conditional Mode (ICM) and provides classification results comparable with the conventional ICM method.