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