Blood vessel detection from fundus image using Markov random field based image segmentation

2017 4th International Conference on Advances in Electrical Engineering (ICAEE) ◽

10.1109/icaee.2017.8255339 ◽

2017 ◽

Author(s):

Nafize Ishtiaque Hossain ◽

Sakib Reza

Keyword(s):

Image Segmentation ◽

Blood Vessel ◽

Random Field ◽

Markov Random Field ◽

Fundus Image ◽

Blood Vessel Detection ◽

Markov Random ◽

Vessel Detection

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Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization

Journal of Structural Biology ◽

10.1016/j.jsb.2003.11.028 ◽

2004 ◽

Vol 145 (1-2) ◽

pp. 123-141 ◽

Author(s):

V Singh

Keyword(s):

Image Segmentation ◽

Random Field ◽

Electron Micrographs ◽

Markov Random Field ◽

Expectation Maximization ◽

Hidden Markov ◽

Particle Identification ◽

Markov Random ◽

Hidden Markov Random Field

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Color Image Segmentation Using Entropy of Fuzziness and Markov Random Field

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)43276-9 ◽

1997 ◽

Vol 30 (7) ◽

pp. 269-274

Author(s):

R. Boussarsar ◽

P. Martin ◽

R. Lecordier ◽

M. Ketata

Keyword(s):

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Color Image ◽

Color Image Segmentation ◽

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Markov random field based image segmentation with adaptive neighborhoods to the detection of fine structures in SAR data

IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.1996.516451 ◽

2002 ◽

Author(s):

P.C. Smits ◽

S. Dellepiane ◽

S.B. Serpico

Keyword(s):

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Markov Random ◽

Fine Structures ◽

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A new conception of image texture and remote sensing image segmentation based on Markov random field

Geo-spatial Information Science ◽

10.1007/s11806-010-0176-2 ◽

2010 ◽

Vol 13 (1) ◽

pp. 16-23 ◽

Author(s):

Yan Gong ◽

Ning Shu ◽

Jili Li ◽

Liqun Lin ◽

Xue Li

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Remote Sensing Image ◽

Image Texture ◽

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Image Segmentation by Markov Random Field Modeling

Image Processing with MATLAB ◽

10.1201/9781420008937-11 ◽

2008 ◽

pp. 295-326

Keyword(s):

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Field Modeling ◽

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Interactive Image Segmentation using Improved Adaptive Markov Random Field Approach

Indian Journal of Science and Technology ◽

10.17485/ijst/2017/v10i6/94636 ◽

2017 ◽

Vol 10 (6) ◽

pp. 1-9

Author(s):

G. S. Gowri ◽

P. Ponmuthuramalingam ◽

◽

Keyword(s):

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Field Approach ◽

Interactive Image Segmentation ◽

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A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features

Computer Analysis of Images and Patterns - Lecture Notes in Computer Science ◽

10.1007/3-540-44692-3_66 ◽

2001 ◽

pp. 547-554 ◽

Author(s):

Zoltan Kato ◽

Ting-Chuen Pong

Keyword(s):

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Texture Features ◽

Markov Random ◽

Color And Texture Features

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Brain MR Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field Theory

Lecture Notes in Computer Science - Medical Imaging and Augmented Reality ◽

10.1007/978-3-540-28626-4_23 ◽

2004 ◽

pp. 188-195 ◽

Author(s):

Yanqiu Feng ◽

Wufan Chen

Keyword(s):

Field Theory ◽

Image Segmentation ◽

Random Field ◽

Fuzzy Clustering ◽

Markov Random Field ◽

Spatial Constraints ◽

Mr Image ◽

Random Field Theory ◽

Markov Random ◽

Mr Image Segmentation

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Markov Random Field Model Based Graduated Penalty Function for Reinforcing Ill-Defined Edges in Color Image Segmentation

Swarm, Evolutionary, and Memetic Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-35380-2_94 ◽

2012 ◽

pp. 802-809

Author(s):

Panda Sucheta ◽

P. K. Nanda

Keyword(s):

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Penalty Function ◽

Field Model ◽

Color Image ◽

Color Image Segmentation ◽

Random Field Model ◽

Markov Random Field Model ◽

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Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field via Bayesian Theory

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1093/ietfec/e91-a.3.723 ◽

2008 ◽

Vol E91-A (3) ◽

pp. 723-729 ◽

Author(s):

X. LI ◽

T. ZHANG ◽

Z. QU

Keyword(s):

Image Segmentation ◽

Random Field ◽

Fuzzy Clustering ◽

Markov Random Field ◽

Bayesian Theory ◽

Spatial Constraints ◽

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