Title :
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
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;
Conference_Titel :
Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on
Print_ISBN :
0-7803-1865-X
DOI :
10.1109/SIPNN.1994.344972
