A new look at finite mixture models in medical image analysis

Author

Wang, Yue ; Lei, Tianhu

Author_Institution

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

fYear

1994

fDate

13-16 Apr 1994

Firstpage

33

Abstract

Presents a new look at finite mixture models in unsupervised medical image analysis. Both the conditional and the standard finite normal mixture models are discussed in detail in terms of physical and mathematical understanding. Based on statistics and information theory, their applications in model selection, parameter quantification and image segmentation are justified and supported by several new theorems and algorithms. Numerical examples with simulated data and real medical images are presented which have shown a great promise

Keywords

image segmentation; medical image processing; medical signal processing; parameter estimation; conditional finite normal mixture model; image segmentation; information theory; medical image analysis; model selection; parameter quantification; real medical images; standard finite normal mixture model; statistics; unsupervised medical image analysis; Biomedical imaging; Gaussian distribution; Hidden Markov models; Image analysis; Image segmentation; Mathematical model; Pixel; Random variables; Statistics; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on

Print_ISBN

0-7803-1865-X

Type

conf

DOI

10.1109/SIPNN.1994.344972

Filename

344972