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

共 50 条

[31] A New Conception of Image Texture and Remote Sensing Image Segmentation Based on Markov Random Field
Gong Yan
Shu Ning
Li Jili
Lin Liqun
Li Xue
GEO-SPATIAL INFORMATION SCIENCE, 2010, 13 (01) : 16 - 23
[32] A New Conception of Image Texture and Remote Sensing Image Segmentation Based on Markov Random Field
GONG Yan1
Geo-Spatial Information Science, 2010, (01) : 16 - 23
[33] Markov random field method for dynamic PET - Image segmentation
Lin, KP
Lou, SL
Yu, CL
Chung, BT
Wu, LC
Liu, RS
MEDICAL IMAGING 1998: IMAGE PROCESSING, PTS 1 AND 2, 1998, 3338 : 1198 - 1204
[34] Unsupervised image segmentation using Markov random field models
Barker, SA
Rayner, PJW
PATTERN RECOGNITION, 2000, 33 (04) : 587 - 602
[35] A region Markov random field model with integrated edge feature and image segmentation algorithm
MOE Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, China
不详
Han, J., 1600, Xi'an Jiaotong University (48): : 14 - 19
[36] A NEW LOCALIZED SUPERPIXEL MARKOV RANDOM FIELD FOR IMAGE SEGMENTATION
Wang, XiaoFeng
Zhang, Xiao-Ping
ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3, 2009, : 642 - 645
[37] An image segmentation approach to extract colon lumen through colonic material tagging and hidden Markov random field model for virtual colonoscopy
Li, LH
Chen, DQ
Lakare, S
Kreeger, K
Bitter, I
Kaufman, AE
Wax, MR
Djuric, PM
Liang, ZG
MEDICAL IMAGING 2002: PHYSIOLOGY AND FUNCTION FROM MULTIDIMENSIONAL IMAGES, 2002, 4683 : 406 - 411
[38] Image compression based on Gaussian mixture model constrained using Markov random field
Sun, Jianjun
Zhao, Yan
Wang, Shigang
Wei, Jian
SIGNAL PROCESSING, 2021, 183
[39] Superpixel Based RGB-D Image Segmentation Using Markov Random Field
Hamedani, Taha
Zarei, Ramin
Harati, Ahad
2015 INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP), 2015, : 89 - 94
[40] A RESEARCH OF TREE IMAGE MARKOV RANDOM FIELD SEGMENTATION METHOD BASED ON GENETIC ALGORITHM
Wang, Xiaosong
Li, Xiurong
Zhiqing, Zheng
Yuan, Li
MECHATRONIC SYSTEMS AND CONTROL, 2023, 51 (03): : 166 - 171

← 1 2 3 4 5 →