Unsupervised medical image analysis by multiscale FNM modeling and MRF relaxation labeling

Author

Wang, Yue ; Adali, Tülay ; Lei, Tianhu

Author_Institution

Dept. of Electr. Eng., Maryland Univ., Baltimore, MD, USA

fYear

1994

fDate

27-29 Oct 1994

Firstpage

101

Abstract

We derive two types of block-wise FNM model for pixel images by incorporating local context. The self-learning is then formulated as an information match problem and solved by first estimating model parameters to initialize ML solution and then conducting finer segmentation through MRF relaxation

Keywords

Markov processes; image matching; image segmentation; maximum likelihood estimation; medical image processing; random processes; unsupervised learning; ML solution; MRF relaxation labeling; Markov random fields; block-wise FNM model; image segmentation; information match problem; local context; multiscale FNM modeling; parameter estimation; pixel images; self-learning; unsupervised medical image analysis; Bayesian methods; Biomedical imaging; Context modeling; Image analysis; Image segmentation; Labeling; Maximum likelihood estimation; Parameter estimation; Pixel; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on

Conference_Location

Alexandria, VA

Print_ISBN

0-7803-2761-6

Type

conf

DOI

10.1109/WITS.1994.513928

Filename

513928

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2346215