Texture based adaptive clustering algorithm for 3D breast lesion segmentation

Segmentation of MRI brain images plays a critical role in medical image processing and analysis. In this paper, a new method based on Markov Random Field (MRF) is proposed for segmentation of MR brain images. We consider the low-level MRF as a linear combination of Gaussians (LCG) with positive and negative component, and we use the modified Expectation-maximization (MEM) algorithm to estimate the mean, variance and proportion for each distribution.The MEM algorithm is sensitive to initial parameters, so we improve the method of initialization. In high-level MRF, we use Potts model to describe the label image. By using the Bayesian maximum a posterior (MAP) rule, the segmentation problem is converted to precisely identify the models parameters. The MAP estimation is obtained using the Metroplis algorithm to search the optimization. The experimental results show that the proposed method is effective for segmentation of MR brain images.

In this article, to get a high-resolution radar image with distributed frequency modulated continuous waveform multiple-input-multiple-output (FMCW MIMO) radar, Bayesian matching pursuit (BMP)-based imaging methods are proposed, in which the received signals at the distributed FMCW MIMO radars are reformulated in terms of the (azimuth, range) patches in the image region of interest and the maximum a posterior (MAP) estimator that can estimate the azimuth angles and ranges of multiple targets is then derived. For a single FMCW MIMO radar, we first propose the BMP-based imaging, in which the support vector (indicating the presence of targets in the radar image patches) and the associated coefficients (the reflection coefficients of the associated targets) are iteratively updated. Because MAP estimation needs the combinatorial search over all possible candidates to find the nonzero elements in the support vector, an approximated MAP estimation method is newly proposed. In addition, by extending the BMP-based imaging for a single FMCW MIMO radar, we develop a new distributed BMP-based imaging method when multiple FMCW MIMO radars are spatially distributed. Through the simulation, we confirm that the proposed BMP-based radar imaging methods outperform the conventional backprojection method or the orthogonal matching pursuit-based method with slightly increased computational complexity.

An algorithm devoted to the segmentation of 3-D ultrasonic data is proposed. The algorithm involves 3-D adaptive clustering based on multiparametric information: the gray-scale intensity of the echographic data, 3-D texture features calculated from the envelope data, and 3-D tissue characterization information calculated from the local frequency spectra of the radio-frequency signals. The segmentation problem is formulated as a Maximum A posterior (MAP) estimation problem. A multi-resolution implementation of the algorithm is proposed. The approach is tested on simulated data and on in vivo echocardiographic 3-D data. The results presented in the paper illustrate the robustness and the accuracy of the proposed approach for the segmentation of ultrasonic data.

Shape-from-focus reconstruction using nonlocal matting Laplacian prior followed by MRF-based refinement

An estimator for the Ricean $K$ factor is designed by approximating the maximum a posterior (MAP) probability. The performance of the approximate MAP estimator is examined. Numerical results show that the approximate MAP estimator outperforms the approximate maximum likelihood estimator. It also outperforms the best moment-based estimator at small sample sizes.

In this paper, we propose multi-spectral fusion and denoising (MFD) of RGB and NIR images using multi-scale wavelet analysis. We formulate MFD of RGB and NIR images as a maximum a posterior (MAP) estimation problem in the wavelet domain. The direct fusion of noisy RGB and NIR image often leads to contrast attenuation due to the discrepancy between RGB and NIR images. Thus, we generate the wavelet scale map for fusion and denoising based on correlation between NIR and RGB wavelet coefficients. To consider local contrast and visibility of NIR data on RGB components, we provide the contrast preservation term for scale map estimation based on the local contrast and visibility. We use the regularization term to select high visibility and contrast of NIR wavelet coefficients in the scale map. Since noise generally appears in the high frequency band, we use gradients of NIR wavelet coefficients as the weight for weighted least square (WLS) smoothing in the scale map. Based on the wavlet scale map, we perform fusion and denoising of RGB and NIR wavelet coefficients. Experimental results show that the proposed method successfully performs fusion of RGB and NIR images with noise reduction and detail preservation as well as outperforms state-of-the-arts in terms of discrete entropy (DE) and feature-based blind image quality evaluator (FBIQE).

Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM), have been recently developed. To capitalize on information available in a fusion process, we propose a Bayesian data fusion approach that incorporates the temporal correlation information in the image time series and casts the fusion problem as an estimation problem in which the fused image is obtained by the Maximum A Posterior (MAP) estimator. The proposed approach provides a formal framework for the fusion of remotely sensed images with a rigorous statistical basis; it imposes no requirements on the number of input image pairs; and it is suitable for heterogeneous landscapes. The approach is empirically tested with both simulated and real-life acquired Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Experimental results demonstrate that the proposed method outperforms STARFM and ESTARFM, especially for heterogeneous landscapes. It produces surface reflectances highly correlated with those of the reference Landsat images. It gives spatio-temporal fusion of remotely sensed images a solid theoretical and empirical foundation that may be extended to solve more complicated image fusion problems.

In this paper, a novel approach has been proposed to register noisy brain multimodal images. Specifically, we have considered CT and MR images. The MR image is the noisy one that has been considered to be the floating image while CT image is the fixed one. The joint histogram of the CT and noisy MR image has been modeled as Markov Random Field (MRF). Every element of the joint histogram has been estimated using the Maximum a Posterior (MAP) estimation criterion. The MAP estimates have been obtained jointly by Simulated Annealing (SA) and Iterative Conditioned Mode (ICM) algorithms. This estimated joint histogram has been used to compute the mutual information for different values of the registration parameter. The registration parameter has been estimated with 99 percent confidence interval. The proposed scheme has been successfully tested with different noise conditions and has been compared with five existing methods.

According to the characteristics of the cubature Kalaman filter, the precision of filter can be improved by considering the statistical characters of the noise, thus an adaptive CKF is applied to solve the estimation problem existed in the semi-strapdown seeker system. By calculating the mean value in each iteration, the algorithm can estimate and correct the statistical characters of the noise on-line by using Sage-Husa maximum a posterior (MAP) estimator in the filtering process therefore, and then can effectively improve the estimation accuracy and stability of the CKF. When the normal and adaptive CKF methods are applied in the target tracking model of infrared semi-strapdown seeker, the simulation results show that the adaptive CKF algorithm is more feasible and effective, and it has better performance than the CKF both in stability and precision, thus demonstrating that the ACKF could obviously improve the filtering effect of normal CKF algorithm.

A new image segmentation algorithm based on Markov random field (MRF) and ant colony system (ACS) is presented in this paper. Information positive feedback and heuristic search, the characters of ACS, were applied for the image segmentations with MRF model. The maximum a posterior (MAP) global best solution of segmentations will be got though MRF, which describes image data relations by local correlations instead of global image possibility distributions. Compared with the simulated annealing (AS), ACS needs less time to search the global best solution. In this paper we proposed a segmentation algorithm combined MRF with ACS, which not only applied ACS as optimization algorithm but also introduced the neighborhood pheromone interaction rules into ACS under MRF model. Especially the pheromone interaction update provided remunerative information to ants in a neighborhood instead of an ant, which could accelerate the optimizing velocity and restrain the relative blur noise. The followed image segmentations experiments proved that this novel algorithm could reach a satisfied result among the noise restraint, edges preservation and computation complexity.

Quantum fluctuation is introduced into the Markov random field (MRF) model for image restoration in the context of a Bayesian approach. We investigate the dependence of the quantum fluctuation on the quality of BW image restoration by making use of statistical mechanics. We find that the maximum posterior marginal (MPM) estimate based on the quantum fluctuation gives a fine restoration in comparison with the maximum a posterior (MAP) estimate or the thermal fluctuation-based MPM estimate.

In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatial-temporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.

Machine learning techniques have gained much interest in reservoir characterization. However, the model prediction often lacks interpretability and reliability due to the 'black-box' nature of, for example, neural networks. In this paper, we present a method to identify core lithofacies and measure the uncertainty by U-net based Bayesian Neural Networks. This approach employs variational dropout and maximum a posterior (MAP) estimate, where we can quantify the uncertainties from both the data and model. We applied the method to predict the lithofacies of cores from the Wilcox Group, Gulf of Mexico. A prediction accuracy of 85% was obtained, and the associated uncertainties were evaluated. Although our current database is not complex and large enough to mimic the realworld scenario, it demonstrates the importance of understanding the uncertainties from data and model. Such uncertainties improve the data predictions and enhance the model reliability. They can be employed to quality control the training data and to improve the performance of the trained model.

Noisy multimodal brain image registration using markov random field model

Studies of intrinsically disordered proteins that lack a stable tertiary structure but still have important biological functions critically rely on computational methods that predict this property based on sequence information. Although a number of fairly successful models for prediction of protein disorder were developed over the last decade, the quality of their predictions is limited by available cases of confirmed disorders. To more reliably estimate protein disorder from protein sequences, an iterative algorithm is proposed that integrates predictions of multiple disorder models without relying on any protein sequences with confirmed disorder annotation. The iterative method alternately provides the maximum a posterior (MAP) estimation of disorder prediction and the maximum-likelihood (ML) estimation of quality of multiple disorder predictors. Experiments on data used at the Critical Assessment of Techniques for Protein Structure Prediction (CASP7 and CASP8) have shown the effectiveness of the proposed algorithm.