EM algorithm for image segmentation initialized by a tree structure scheme

Author

Fwu, Jong-Kae ; Djuric, Petar M.

Author_Institution

Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY, USA

Volume

6

Issue

2

fYear

1997

fDate

2/1/1997 12:00:00 AM

Firstpage

349

Lastpage

352

Abstract

In this correspondence, the objective is to segment vector images, which are modeled as multivariate finite mixtures. The underlying images are characterized by Markov random fields (MRFs), and the applied segmentation procedure is based on the expectation-maximization (EM) technique. We propose an initialization procedure that does not require any prior information and yet provides excellent initial estimates for the EM method. The performance of the overall segmentation is demonstrated by segmentation of simulated one-dimensional (1D) and multidimensional magnetic resonance (MR) brain images

Keywords

Markov processes; biomedical NMR; brain; image segmentation; medical image processing; random processes; trees (mathematics); 1D magnetic resonance brain images; EM algorithm; Markov random fields; expectation-maximization technique; image segmentation; initial estimates; initialization procedure; multidimensional magnetic resonance brain images; multivariate finite mixtures; tree structure scheme; vector images; Brain modeling; Image segmentation; Iterative algorithms; Iterative methods; Markov random fields; Parameter estimation; Pixel; Positron emission tomography; Training data; Tree data structures;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.551709

Filename

551709