A parallel image segmentation algorithm using relaxation with varying neighborhoods and its mapping to array processors

Unsupervised segmentation of noisy and textured images using Markov random fields

The present paper is concerned with the study of parallel approaches for the relaxation algorithms associated to Markov Random Fields (MRF) models in image analysis, [2]. MRF-based relaxation algorithms are known to be intrinsically parallel and massive parallelizations by updating at once independent variables of the field have been early described, [4]. We present here a different approach based on relaxation algorithms running in parallel at different and interacting periodically. The proposed approach is based on parallel Markov chains which have been studied by Aarts et al., [l], and Graffigne, [3], for global optimization using simulated annealing. In our method, parallel Markov chains are coupled with a new multiscale exploration of the configuration space for MRF. This multiscale exploration can be implemented efficiently by considering a sequence of multiresolution MRF models whose parameters and neighborhood structures are obtained from the original MRF model. The designed algorithm is suited for MIMD computers and can be outlined as follows. First MRF models at different scales are computed from the original MRF model. The relaxations at different scales are stochastic and cooperative : every p iterations - one iteration corresponding to a full sweep on the image - a processor attempts to transfer local estimates to the processor controlling the next finer scale. With the low resolution levels of the hierarchy are associated high temperatures in the stochastic relaxation, [3]. A high temperature allows to escape from local minima of the energy function. With the intermediate resolution levels are associated lower temperatures. At these levels, the relaxation process becomes more sensitive to local minima and visits the large or medium size valleys of the energy landscape. At the finest resolution level deterministic relaxation is adopted : the estimation is ultimately refined at that level. The proposed parallelization approach is compared to sequential deterministic and stochastic relaxation in the context of optical flow estimation. The multiscale algorithm exhibits fast convergence properties, comparable to multigrid deterministic relaxation, and the obtained estimates are close to the near optimal solutions obtained by time consuming sequential stochastic methods.

Markov random field method is a very active research field in image segmentation. In this paper, a new approach to the segmentation of SAR target chip image based on the Markov random field (MRF) and Maximum a Posteriori (MAP) rule is proposed. By statistical analysis of the different regions in the SAR target chip image, the valid segmentation algorithm for choosing the iteration initial value is obtained. Compared with other segmentation algorithms such as Simulate Annealing algorithm and the Iterative Conditional Model (ICM) algorithm by the synthetic image and operational SAR image, the proposed approach in the paper can accelerate the convergence speed, reduce the iterative numbers and improve the segmentation speed.

In order to overcome the poor anti-noise performance of traditional fuzzy C-Means (FCM) algorithm in image segmentation, a novel improved FCM algorithm was proposed in this paper based on Particle Swarm Optimization (PSO) algorithm and Markov Random Field (MRF) model, which can make full use of the global searching ability of PSO and the spatial information integrating ability of MRF for image segmentation. In this algorithm, the image segmentation is converted to a PSO optimization problem, in which the fitness function is set up to containing the spatial information based on the spectral value and the neighboring pixels modeled by MRFs. And segmentation results can be iteratively obtained during the PSO iterations according to the newly designed membership function of FCM in which the spatial information is integrated. The experiments herein reported in this paper illustrate the better performance of this algorithm than the traditional FCM algorithm and the PSO algorithm for image segmentation.

Segmentation is a low-level processing aimed at the partition of an image in disjoint regions, each one homogeneous with respect to some properties like intensity, texture, shape, etc. Such a task is needed in many high-level processing and applications in such diverse fields as remote-sensing, medical imaging, video coding, and industrial automation, just to name a few. In this work of thesis, we first deal with the problem of color based image segmentation, that is based on some form of homogeneity in the color/spectral properties of the image, and then move to the more complex task of texture based segmentation, where the aim is to recognize complex structures in the image which are typically non homogeneous in terms of spectral properties. Concerning color based segmentation, we focus on a specific class of Markov Random Field (MRF) probability models, namely the Tree-Structured Markov Random Fields (TS-MRF), recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF. As a first contribution, we first extend the model to generic tree structures removing the unnecessary binary constraint, and then devise a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node, and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique, which makes use of a variable-bandwidth strategy based on the k-Nearest Neighbors (k-NN) technique, and is implemented with a speed-up strategy that cuts significantly the computational complexity. Experimental results on synthetic data and real life images from the remote sensing domain prove the potential of the proposed method. Turning to texture based segmentation, we present a novel multiscale texture model, and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled in turn by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The ``fragmentation'' step allows one to find the elementary textures of the model, while the ``reconstruction'' step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural images from the Berkeley dataset. Finally, an application of the TFR algorithm for the unsupervised segmentation of multiresolution remotely sensed images is proposed, based on the significant presence of textural content in last generation high-resolution satellite images. The obtained results further assess the potential of the proposed method for such real life applications.

This paper presents a new two-step pseudo maximum a posteriori (MAP) segmentation method for the Markov random field (MRF)-modeled image because the exact MAP estimation is hard to implement due to intractable complexity. The expectation maximization (EM) and Markov Chain Monte Carlo (MCMC) methods are adopted to estimate the parameters for the MRF model due to their comparatively good performance. Although the image segmentation algorithms via graph cuts have become very popular nowadays, our proposed algorithm still performs significantly better in automatic identification and segmentation of fuzzy images than them, which is shown by the quantitative results on synthesized images. In practical applications, the proposed two-step pseudo MAP method is superior in segmenting the fuzzy laser images reflected from the weld pool surfaces during the P-GMAW welding process.

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.

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Improving the runtime of MRF based method for MRI brain segmentation

A robust and adaptive algorithm for segmenting a noisy image is presented. The original (or noise-free) images are modeled by an underlying Gibbs random field (GRF) or equivalently by a Markov random field (MRF), and the driving noise is a mixture of an additive independent Gaussian noise and an outlier process. The processes of maximum a posteriori (MAP) segmentation and maximum-likelihood (ML) estimation for the image model parameters are carried out simultaneously. Both procedures, which are based on stochastic relaxation algorithms with a simulated annealing technique, are efficiently implementable on parallel architectures, such as artificial neural networks. Our proposed method is adaptive in that it recursively segments the noise-corrupted images and estimates the model parameters necessary for the segmentation procedure. We should emphasize that the proposed algorithm is robust in eliminating the effect caused by outliers. Finally, a number of computer simulation experiments are given to demonstrate the robustness and effectiveness of the recursive algorithm.

A Validation Framework for Brain Tumor Segmentation

Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context, the present paper proposes an unsupervised and graph-based method of image segmentation, which is driven by an application goal, namely, the generation of image segments associated with a user-defined and application-specific goal. A graph, together with a random grid of source elements, is defined on top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven predicate and a suited parametric model for the data. In the experimental validation with both magnetic resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated products.

Often, moving object detection in a video sequence has been achieved a variant of temporal segmentation methods. For slow moving video objects, a temporal segmentation method fails to detect the objects. In this paper, we propose a Markov random Field (MRF) model based scheme to detect slow movements in a video sequence. The proposed scheme is a combination of a proposed spatio-temporal segmentation scheme and temporal segmentation method. A compound MRF model is used in spatio-temporal framework. In this framework, the a priori distribution is MRF and this takes care of spatial distribution of current frame, temporal frames and the Change Detection Masks (CDM) of the temporal frames. The spatio-temporal segmentation problem is formulated as a pixel labeling problem in Maximum a posteriori (MAP) framework. The MAP estimates are obtained using a hybrid algorithm. These estimated labels are used to obtain the Video Object Plane (VOP) and hence the detection of objects. The results are compared with joint segmentation scheme (JSEG). Results presented demonstrate that the proposed scheme with CDM model could detect slow moving video objects.

Comparison of thresholding techniques for quantifying portland cement hydrates using synchrotron microtomography

Combining Bayesian network (BN) and Markov random field (MRF) models, this paper presents an effective supervised image segmentation algorithm. Representing information from different features, a Bayesian network generates the probability map for each pixel via the conditional PDF (probability density function) learned from a limited training data set. Considering the spatial relation and a priori knowledge of the image, MRF theory is used to generate a reasonable segmentation by minimizing the proposed energy functional. Applying this algorithm to multi-contrast MR image in vivo plaque composition measurement shows comparable results with expert manual segmentation.