Color image segmentation based on Markov random field clustering for histological image analysis

2003 ◽  
Author(s):  
V. Meas-Yedid ◽  
S. Tilie ◽  
J.-C. Olivo-Marin
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Random Field ◽  
Markov Random Field ◽  
Color Image ◽  
Color Image Segmentation ◽  
Histological Image ◽  
Markov Random

Color Image Segmentation Using Entropy of Fuzziness and Markov Random Field

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 ◽  
Markov Random

Constrained Compound Markov Random Field Model with Graduated Penalty Function for Color Image Segmentation

2011 ◽  
Vol 403-408 ◽  
pp. 3438-3445
Author(s):  
Sucheta Panda ◽  
P.K. Nanda
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Markov Random Field ◽  
Color Image ◽  
A Priori ◽  
Color Space ◽  
Color Image Segmentation ◽  
Misclassification Error ◽  
Model Parameters ◽  
Markov Random

In this paper, an unsupervised color image segmentation scheme has been proposed for preserving strong and weak edges as well. A Constrained Compound Markov Random Field (MRF) has been proposed as the a priori model for the color labels. We have used Ohta (I1, I2, I3) color model and a controlled correlation of the color space has been accomplished by the proposed compound MRF model. The Constrained Compound MRF (CCMRF) is found to possess the unifying property of modeling scenes as well as color textures. In unsupervised scheme, the associated model parameters and the image labels are estimated recursively. The model parameters are the Maximum Conditional Pseudo Likelihood (MCPL) estimates and the labels are the Maximum a Posteriori (MAP) estimates. The performance of the proposed scheme has been compared with that of Yu’s method and has been found to exhibit improved performance in the context of misclassification error.

scholarly journals DNA Microarray Image Segmentation Using Markov Random Field Algorithm

2021 ◽  
Vol 2071 (1) ◽  
pp. 012032
Author(s):  
K A Mat Said ◽  
A B Jambek
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Random Field ◽  
Dna Microarray ◽  
Markov Random Field ◽  
Deoxyribonucleic Acid ◽  
Experimental Results ◽  
Microarray Image ◽  
Markov Random ◽  
Stage Process

Abstract A deoxyribonucleic acid (DNA) microarray image requires a three-stage process to enhance and preserve the image’s important information. These are gridding, segmentation, and intensity extraction. Of these three processes, segmentation is considered the most difficult, as its function is to differentiate between features in the foreground and background. The elements in the foreground form the object or the vital information of the image, while the background features less critical information for DNA microarray image analysis. This paper presents a study that utilises the Markov random field (MRF) segmentation algorithm on a DNA microarray image. The MRF algorithm evaluates the current pixel depends on its neighbouring pixels. The experimental results show that the MRF algorithm works effectively in the segmentation process for a DNA microarray image.

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