FMRI Image Segmentation Based on Hidden Markov Random Field with Directional Statistics Observation Model

被引：0

作者：

Chernyaev, S. D. ^{[1
]}

Lukashenko, O., V ^{[1
,2
]}

机构：

[1] Petrozavodsk State Univ, Petrozavodsk, Russia

[2] Russian Acad Sci, Inst Appl Math Res, Karelian Res Ctr, Petrozavodsk, Russia

来源：

TWELFTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2019) | 2020年 / 11433卷

基金：

俄罗斯基础研究基金会;

关键词：

fMRI; segmentation; Markov random field; von Mises-Fisher distribution; Bayesian inference; EXPECTATION-MAXIMIZATION; ENERGY MINIMIZATION;

D O I：

10.1117/12.2559545

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this paper we consider the problem of segmentation of three-dimensional fMRI images within the Bayesian framework with Markov Random Field (MRF) as the prior distribution and von Mises-Fisher distribution as the likelihood. Usually, the learning of such models is a complicated task and the exact inference is impossible in practice. To fit the proposed model, we apply the mean field approximation on the inference step in the EM algorithm. Some numerical examples are presented to illustrate the proposed method.

引用

页数：8

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