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Patient-specific cerebrovascular modeling provides essential information to facilitate the identification of vessel-free trajectories in functional neurosurgery. However, standard gadolinium models used clinically are often incomplete due to the extent of manual labor required to segment the vessels and because gadolinium contrast decreases rapidly with vessel size. In this work, we propose an automatic method, based on the Markov Random Field (MRF) theory, to segment venous blood vessels from dense susceptibility-weighted imaging (SWI) venography datasets. Unlike conventional isotropic auto-logistic MRF, our MRF design anisotropically favors the neighboring influence of voxels classified as ˵vessels″ to better preserve thin vessels imaged by SWI. Results show that MRF segmentation of deep veins compares well with standard scale-space vesselness analysis. Most importantly, we demonstrate automatic segmentation of superficial veins on SWI and creation of denser 3D vascular models that may improve clinical gadolinium-based models. [ABSTRACT FROM AUTHOR]

We consider the problem of using graph-theoretical techniques to predict the function of unannotated proteins in an organism΄s proteome. Specifically, we present an overview of the major methods for predicting protein function based on interaction network structure and describe an abstract framework within which these methods can be treated in a unified fashion. We also present a comparison of the proposed methods and highlight some open theoretical and practical questions in the area. [ABSTRACT FROM AUTHOR]

An algorithm is created, which performs human gait analysis using spatial data and amplitude images from a Time-of-flight camera. For each frame in a sequence the camera supplies cartesian coordinates in space for every pixel. By using an articulated model the subject pose is estimated in the depth map in each frame. The pose estimation is based on likelihood, contrast in the amplitude image, smoothness and a shape prior used to solve a Markov random field. Based on the pose estimates, and the prior that movement is locally smooth, a sequential model is created, and a gait analysis is done on this model. The output data are: Speed, Cadence (steps per minute), Step length, Stride length (stride being two consecutive steps also known as a gait cycle), and Range of motion (angles of joints). The created system produces good output data of the described output parameters and requires no user interaction. [ABSTRACT FROM AUTHOR]

We present a system that analyzes human gait using a treadmill and a Time-of-flight camera. The camera provides spatial data with local intensity measures of the scene, and data are collected over several gait cycles. These data are then used to model and analyze the gait. For each frame the spatial data and the intensity image are used to fit an articulated model to the data using a Markov random field. To solve occlusion issues the model movement is smoothened providing the missing data for the occluded parts. The created model is then cut into cycles, which are matched and through Fourier fitting a cyclic model is created. The output data are: Speed, Cadence, Step length and Range-of-motion. The described output parameters are computed with no user interaction using a setup with no requirements to neither background nor subject clothing. [ABSTRACT FROM AUTHOR]

In minimally invasive surgery, dense 3D surface reconstruction is important for surgical navigation and integrating pre- and intra-operative data. Despite recent developments in 3D tissue deformation techniques, their general applicability is limited by specific constraints and underlying assumptions. The need for accurate and robust tissue deformation recovery has motivated research into fusing multiple visual cues for depth recovery. In this paper, a Markov Random Field (MRF) based Bayesian belief propagation framework has been proposed for the fusion of different depth cues. By using the underlying MRF structure to ensure spatial continuity in an image, the proposed method offers the possibility of inferring surface depth by fusing the posterior node probabilities in a node΄s Markov blanket together with the monocular and stereo depth maps. Detailed phantom validation and in vivo results are provided to demonstrate the accuracy, robustness, and practical value of the technique. [ABSTRACT FROM AUTHOR]

Image Registration is central to different applications such as medical analysis, biomedical systems, image guidance, etc. In this paper we propose a new algorithm for multi-modal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on linear intensity transformation functions. The coefficients of these transformations are represented as prior information by means of Markov random fields. This probabilistic approach allows one to find optimal estimators by minimizing an energy function in terms of both the parameters that control the affine transformation of one of the images and the coefficient fields of the intensity transformations for each pixel. [ABSTRACT FROM AUTHOR]

Standard 3D ranging and imaging systems process only a single return from an assumed single opaque surface. However, there are situations when the laser return consists of multiple peaks due to the footprint of the beam impinging on a target with surfaces distributed in depth or with semi-transparent surfaces. If all these returns are processed, a more informative multi-layered 3D image is created. We propose a unified theory of pixel processing for ladar data using a Bayesian approach that incorporates spatial constraints through a Markov Random Field. The different parameters of the several returns are estimated using reversible jump Markov chain Monte Carlo (RJMCMC) techniques in combination with an adaptive strategy of delayed rejection to improve the estimates of the parameters. [ABSTRACT FROM AUTHOR]

This paper presents an extension to our work on estimating the probability distribution by using a Markov Random Field (MRF) model in an Estimation of Distribution Algorithm (EDA) [1]. We propose a method that directly samples a MRF model to generate new population. We also present a new EDA, called the Distribution Estimation Using MRF with direct sampling (DEUMd), that uses this method, and iteratively refines the probability distribution to generate better solutions. Our experiments show that the direct sampling of a MRF model as estimation of distribution provides a significant advantage over other techniques on problems where a univariate EDA is typically used. [ABSTRACT FROM AUTHOR]

A definition of the term "Markov random field" is presented. It refers to a random field that satisfies a generalization of the Markov property.