Markov random field

About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.

Markov random field for tumor detection in digital mammography

Abstract: A technique is proposed for the detection of tumors in digital mammography. Detection is performed in two steps: segmentation and classification. In segmentation, regions of interest are first extracted from the images by adaptive thresholding. A further reliable segmentation is achieved by a modified Markov random field (MRF) model-based method. In classification, the MRF segmented regions are classified into suspicious and normal by a fuzzy binary decision tree based on a series of radiographic, density-related features. A set of normal (50) and abnormal (45) screen/film mammograms were tested. The latter contained 48 biopsy proven, malignant masses of various types and subtlety. The detection accuracy of the algorithm was evaluated by means of a free response receiver operating characteristic curve which shows the relationship between the detection of true positive masses and the number of false positive alarms per image. The results indicated that a 90% sensitivity can be achieved in the detection of different types of masses at the expense of two falsely detected signals per image. The algorithm was notably successful in the detection of minimal cancers manifested by masses /spl les/10 mm in size. For the 16 such cases in the authors' dataset, a 94% sensitivity was observed with 1.5 false alarms per image. An extensive study of the effects of the algorithm's parameters on its sensitivity and specificity was also performed in order to optimize the method for a clinical, observer performance study. >

Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images

Abstract: The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density. To ensure nonnegativity of the solution, a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates. These methods generally achieve effective convergence in 15-25 iterations. Reconstructions are presented of an /sup 18/FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors. >

Contextual classification with functional Max-Margin Markov Networks

Abstract: We address the problem of label assignment in computer vision: given a novel 3D or 2D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To this end, the Markov Random Field framework has proven to be a model of choice as it uses contextual information to yield improved classification results over locally independent classifiers. In this work we adapt a functional gradient approach for learning high-dimensional parameters of random fields in order to perform discrete, multi-label classification. With this approach we can learn robust models involving high-order interactions better than the previously used learning method. We validate the approach in the context of point cloud classification and improve the state of the art. In addition, we successfully demonstrate the generality of the approach on the challenging vision problem of recovering 3-D geometric surfaces from images.

Markov random fields : theory and application

Abstract: Image modeling during the 1980s - a brief overview, A Rosenfeld compound Gauss-Markov random fields for parallel image processing, JW Woods, et al stochastic algorithms for restricted image spaces and experiments in deblurring, D Geman, et al a continuation method for image estimation using the A-diabatic approximation, A Rangarahan and R Chellappa isotropic priors for single photon emission computed tomography, S Geman Gaussian Markov random fields at multiple resolution, S Lakshmanan and H Derin texture synthesis and classification, S Chatterjee spectral estimation for random fields with applications to Markov Modelling and texture classification, J Yuan and TS Rao probabilistic network inference for cooperative high and low level vision, PB Chow, et al stereo matching, S Barnard 3-D analysis of A shaded and textural surface image, RL Kashyap shape from texture using Gaussian Markov random fields, FS Cohen and M Patel the use of Markov random fields in estimating and reorganizing objects in 3D spaces, DB Cooper, et al A Markov random field model-based approach to image interpretation, JW Modestino and J Zhang A Markov random field restoration of image sequences, TJ Hainsworth and KV Mardia the MIT vision machine - progress in the integration of vision modules, T Poggio and D Weinshall parameter estimation for Gibbs distributions from fully observed data, B Gidas on sampling methods and annealing algorithms, SB Gelfand and S Mitter adaptive Gibbsian automata, JL Marroquin and A Ramirez

The theory and practice of Bayesian image labeling

Abstract: Integrating disparate sources of information has been recognized as one of the keys to the success of general purpose vision systems. Image clues such as shading, texture, stereo disparities and image flows provide uncertain, local and incomplete information about the three-dimensional scene. Spatial a priori knowledge plays the role of filling in missing information and smoothing out noise. This thesis proposes a solution to the longstanding open problem of visual integration. It reports a framework, based on Bayesian probability theory, for computing an intermediate representation of the scene from disparate sources of information. The computation is formulated as a labeling problem. Local visual observations for each image entity are reported as label likelihoods. They are combined consistently and coherently on hierarchically structured label trees with a new, computationally simple procedure. The pooled label likelihoods are fused with the a priori spatial knowledge encoded as Markov Random Fields (MRF's). The a posteriori distribution of the labelings are thus derived in a Bayesian formalism. A new inference method, Highest Confidence First (HCF) estimation, is used to infer a unique labeling from the a posteriori distribution. Unlike previous inference methods based on the MRF formalism, HCF is computationally efficient and predictable while meeting the principles of graceful degradation and least commitment. The results of the inference process are consistent with both observable evidence and a priori knowledge. The effectiveness of the approach is demonstrated with experiments on two image analysis problems: intensity edge detection and surface reconstruction. For edge detection, likelihood outputs from a set of local edge operators are integrated with a priori knowledge represented as an MRF probability distribution. For surface reconstruction, intensity information is integrated with sparse depth measurements and a priori knowledge. Coupled MRF's provide a unified treatment of surface reconstruction and segmentation, and an extension of HCF implements a solution method. Experiments using real image and depth data yield robust results. The framework can also be generalized to higher-level vision problems, as well as to other domains.