Multiresolution Markov Random Field Model Research Articles

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain* *Project supported by the National Natural Science Foundation of China (Grant No. 11471004), and the Key Research and Development Program of Shaanxi Province, China (Grant No. 2018SF-251).

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.

Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov Random Field in wavelet domain

ABSTRACT This paper proposes an unsupervised change detection method for very-high-resolution (VHR) remote sensing images based on multi-resolution Markov random field (MRF) model in wavelet domain. Firstly, the wavelet transform is performed on the difference image achieved by the change vector analysis (CVA) method, and the wavelet coefficients at each scale are obtained. Then, MRF model is constructed based on the wavelet coefficients. The wavelet high-frequency coefficients establish a feature field model that describes the feature attributes of each pixel location at each scale. The initial change map (changed and unchanged) at the coarse scale are generated through applying the k-means method to the wavelet low-frequency coefficients, and a label field model describing the region of the variation results is established. The label and feature field, at the same scale, got the optimized change map under the Bayesian criterion. Finally, the results of the low-resolution scale change map are directly projected as the adjacent higher-scale initial change map. The more accurate change map is obtained successively from the coarse scale to the original resolution scale, and the detection result of the original resolution is obtained at last. Experiments on Quick Bird, SPOT-5, and IKONOS optical images have demonstrated the effectiveness of the proposed method. The experimental results show that the method has better regional consistency and strong robustness.

Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain

Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain

Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

In this paper, we propose a new approach to intelligently segment jacquard warp-knitted fabric images by combining wavelet texture decomposition, multiresolution Markov random field (MRF) modeling and Bayesian parameter estimation. Firstly, we use a flat scanner to capture eight-bit grayscale images of jacquard fabrics and adopt the Gaussian low-pass filter to decrease pixel variation due to discordant light reflection arising from uneven jacquard fabric surface. To overcome the incapacity of single resolution, multiresolution wavelet texture decomposition is employed, inspired by human visual sense procedure. Next, both intra-scale and inter-scale information are taken into account by the MRF model, in which a modified feature field model of the MRF, containing spatial noise with zero-mean Gaussian distribution, is presented in light of the inherent characteristics of jacquard warp-knitted fabric image. Afterward, an adaptive weighting function is used to weaken the defect of the potential parameter set empirically during the process of parameter estimation and image segmentation. Experimental results, used to verify the performance of the proposed algorithm especially the main novelties, prove that the approach is feasible and applicable.

Texture Image Segmentation Based on MRMRF in Contourlet Domain

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

Image segmentation based on multiresolution Markov random field with fuzzy constraint in wavelet domain

This study proposes a multiresolution Markov random field model with fuzzy constraint in wavelet domain (MRMRF-F). In this model, a fuzzy field is introduced into the multiresolution Markov random field model to estimate parameters, by which the spatial constraint between neighbouring features can be reflected. There are three subfields on each resolution in the MRMRF-F model: one feature field, one label field and one fuzzy field. Among these fields, a three-step iteration scheme is designed to realise image segmentation. Namely, the label field renews the fuzzy field; then the fuzzy field estimates the parameters of the feature field; and a renewed label field is obtained by maximising the products of both the probability of the feature field and the probability of current label field; image segmentation is finally obtained by the cycle iteration among these fields. Texture and natural images are used to test the performance of the new model, and experimental results illustrate a better and higher accuracy compared with other existing methods about fuzzy methods or Markov random field models.

A Hybrid System for Detecting Masses in Mammographic Images

This paper discusses the hybrid system for detecting masses in mammographic images. The proposed approach analyzes mammograms in three major steps. First, a global segmentation method is applied to find regions of interest. This step uses texture features, decision trees, and a multiresolution Markov random field model. The second stage works on the output of the previous algorithm. Here, a combination of three different local segmentation methods is used, and then, some relevant features are extracted. Some of them refer to the shape of the object; others are texture parameters. The final decision is made using a linear combination of these features