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.

Image Processing with Matlab: Applications in Medicine and Biology

Abstract: Medical Imaging Systems Fundamental Tools for Image Processing and Analysis Probability Theory for Stochastic Modeling of Images Two-Dimensional Fourier Transform Nonlinear Diffusion Filtering Intensity-Based Image Segmentation Image Segmentation by Markov Random Field Modeling Deformable Models Image Analysis Application 1: Quantification of Green Fluorescent Protein eXpression in Live Cells: ProXcell Application 2: Calculation of Performance Parameters of Gamma Cameras and SPECT Systems Application 3: Analysis of Islet Cells Using Automated Color Image Analysis Appendix A: Notation Appendix B: Working with DICOM Images Appendix C: Medical Image Processing Toolbox Appendix D: Description of Image Data

MRI Bone Segmentation Using Deformable Models and Shape Priors

Abstract: This paper addresses the problem of automatically segmenting bone structures in low resolution clinical MRI datasets. The novel aspect of the proposed method is the combination of physically-based deformable models with shape priors. Models evolve under the influence of forces that exploit image information and prior knowledge on shape variations. The prior defines a Principal Component Analysis (PCA) of global shape variations and a Markov Random Field (MRF) of local deformations, imposing spatial restrictions in shapes evolution. For a better efficiency, various levels of details are considered and the differential equations system is solved by a fast implicit integration scheme. The result is an automatic multilevel segmentation procedure effective with low resolution images. Experiments on femur and hip bones segmentation from clinical MRI depict a promising approach (mean accuracy: 1.44±1.1 mm, computation time: 2mn43s).

On parallel stereo

Abstract: We review some of the open issues in computational stereo. In particular, we will discuss the problem of extracting better matching primitives and of dealing with occlusions. Markov Random Field models - an extension of standard regularization - suggest sophisticated stereo matching algorithms. They are, however, ill-suited to efficient, real-time applications. We will conclude reviewing a new simple but fast algorithm implemented by one of us (Drumheller, 1986) on the TMC Connection Machine (TM) computer. Some of its features are: (a) the potential for combining different primitives, including color information; (b) the use of a stronger and new formulation of the uniqueness constraint; and (c) its disparity representation that maps efficiently into the architecture of the Connection Machine computer.

Segmentation and tracking of interacting human body parts under occlusion and shadowing

Abstract: The paper presents a system to segment and track multiple body parts of interacting humans in the presence of mutual occlusion and shadow. The color image sequence is processed at three levels: pixel level, blob level, and object level. A Gaussian mixture model is used at the pixel level to train and classify individual pixel colors. A Markov random field (MRF) framework is used at the blob level to merge the pixels into coherent blobs and to register inter-blob relations. A coarse model of the human body is applied at the object level as empirical domain knowledge to resolve ambiguity due to occlusion and to recover from intermittent tracking failures. A two-fold tracking scheme is used which consists of blob to blob matching in consecutive frames and blob to body part association within a frame. The tracking scheme resembles a multi-target, multi-assignment framework. The result is a tracking system that simultaneously segments and tracks multiple body parts of interacting people. Example sequences illustrate the success of the proposed paradigm.