scholarly journals A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI

NeuroImage ◽  
2013 ◽  
Vol 65 ◽  
pp. 83-96 ◽  
Author(s):  
Srikanth Ryali ◽  
Tianwen Chen ◽  
Kaustubh Supekar ◽  
Vinod Menon
Keyword(s):  
Resting State ◽  
Random Fields ◽  
Markov Random Fields ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Markov Random ◽  
Von Mises

scholarly journals Comparative Analysis of Methods for Segmentation of FMRI Images Based on Markov Random Fields

2019 ◽  
Author(s):  
Сергей Черняев ◽  
Sergey Chernyaev ◽  
Олег Лукашенко ◽  
Oleg Lukashenko
Keyword(s):  
Statistical Model ◽  
Random Fields ◽  
Markov Random Fields ◽  
Three Dimensional ◽  
Training Data ◽  
Model Parameters ◽  
Fisher Distribution ◽  
Markov Random ◽  
Von Mises ◽  
The Bayesian Approach

The problem of segmentation of three-dimensional fMRI images based on the Bayesian approach is considered, where Markov Random Field is used as the prior distribution, and von Mises-Fisher distribution is used as the observation model. The main problem when applying this approach in practice is an estimation of the model parameters. In this paper, we review algorithms HMRF-MCEM, HMRF-EM and GrabCut, which implement this statistical model and estimate parameters without the usage of the labeled training data. The methods HMRF-EM and GrabCut were introduced in conjunction with other statistical models, but after a small modification, they can be used with the von Mises-Fisher distribution. A comparative study was carried out by performing experiments on both synthetic, generated from the statistical model, and real fMRI data.

Sampling from Gaussian Markov random fields conditioned on linear constraints

2008 ◽  
Vol 48 ◽  
pp. 1041 ◽  
Author(s):  
Daniel Peter Simpson ◽  
Ian W. Turner ◽  
A. N. Pettitt
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Linear Constraints ◽  
Gaussian Markov Random Fields ◽  
Markov Random

scholarly journals A Methodology for Redesigning Networks by Using Markov Random Fields

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1389
Author(s):  
Julia García Cabello ◽  
Pedro A. Castillo ◽  
Maria-del-Carmen Aguilar-Luzon ◽  
Francisco Chiclana ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Random Fields ◽  
Markov Random Fields ◽  
Application Performance ◽  
Cross Border ◽  
Markov Random ◽  
User Groups ◽  
Universal Definition ◽  
Definition Of ◽  
Decision Making Model

Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.

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