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Conference material: "Proceedings of the International Conference on Computer Graphics and Vision Graphicon"
Authors: Chernyaev S.D., Lukashenko O.V.
Comparative Analysis of Methods for Segmentation of FMRI Images Based on Markov Random Fields
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.
fMRI, segmentation, Markov random field, von Mises-Fisher distribution, Bayesian inference
Publication language: russian,  pages: 5 (p. 143-147)
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About authors:
  • Chernyaev Sergey Dmitrievich,  ,  Petrozavodsk State University
  • Lukashenko Oleg Viktorovich,  ,  Petrozavodsk State University; Institute of Applied Mathematical Research of Karelian Research Centre of Russian Academy of Sciences