DocumentCode
419787
Title
Expectation-maximization for a linear combination of Gaussians
Author
Gimel´farb, Georgy ; Farag, Aly A. ; El-Baz, Ayman
Author_Institution
Dept. of Comput. Sci., Auckland Univ., New Zealand
Volume
3
fYear
2004
fDate
23-26 Aug. 2004
Firstpage
422
Abstract
We propose a modified expectation-maximization algorithm that approximates an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). Due to both positive and negative components, the LCG approximates inter-class transitions more accurately than a conventional mixture of only positive Gaussians. Experiments in segmenting multi-modal medical images show the proposed LCG-approximation results in more adequate region borders.
Keywords
Gaussian processes; approximation theory; image segmentation; medical image processing; optimisation; probability; approximation theory; empirical probability density function; expectation maximization algorithm; linear combination of Gaussians; multimodal medical image segmentation; Character generation; Computer science; Frequency; Gaussian approximation; Gaussian processes; Image segmentation; Parameter estimation; Pattern recognition; Probability density function; Probability distribution;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-2128-2
Type
conf
DOI
10.1109/ICPR.2004.1334556
Filename
1334556
Link To Document