DocumentCode :
2087453
Title :
Parameter estimation and segmentation of noisy or textured images using the EM algorithm and MPM estimation
Author :
Comer, Mary L. ; Delp, Edward J.
Author_Institution :
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA
Volume :
2
fYear :
1994
fDate :
13-16 Nov 1994
Firstpage :
650
Abstract :
Presents a new algorithm for segmentation of noisy or textured images using the expectation-maximization (EM) algorithm for estimating parameters of the probability mass function of the pixel class labels and the maximization of the posterior marginals (MPM) criterion for the segmentation operation. A Markov random field (MRF) model is used for the pixel class labels. The authors present experimental results demonstrating the use of the new algorithm on synthetic images and medical imagery
Keywords :
Markov processes; image segmentation; image texture; optimisation; parameter estimation; probability; random processes; Markov random field; expectation-maximization algorithm; maximization of the posterior marginals criterion; medical imagery; noisy images; parameter estimation; pixel class labels; segmentation; segmentation operation; synthetic images; textured images; Biomedical imaging; Computer vision; Image processing; Image segmentation; Laboratories; Markov random fields; Parameter estimation; Pixel; Probability; Tellurium;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference
Conference_Location :
Austin, TX
Print_ISBN :
0-8186-6952-7
Type :
conf
DOI :
10.1109/ICIP.1994.413651
Filename :
413651
Link To Document :
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