TY - GEN
T1 - Unsupervised Bayesian classifier applied to the segmentation of retina image
AU - Banga, C.
AU - Ghorbel, F.
AU - Pieczynski, W.
N1 - Publisher Copyright:
© 1992 IEEE.
PY - 1992/1/1
Y1 - 1992/1/1
N2 - In this paper, we use a stochastic model based on the finite normal mixture distribution identification for retina image segmentation. Local unsupervised methods blind and contextual, using the Expectation-Maximisation (EM) family algorithms for parameter estimation are tested. To get rid of the spatial dependence effect of pixels, a decorrelation processing is used before parameter estimation. The segmentation is then performed by Bayesian decision rule. Segmentation results are presented to prove the effectiveness of different approaches.
AB - In this paper, we use a stochastic model based on the finite normal mixture distribution identification for retina image segmentation. Local unsupervised methods blind and contextual, using the Expectation-Maximisation (EM) family algorithms for parameter estimation are tested. To get rid of the spatial dependence effect of pixels, a decorrelation processing is used before parameter estimation. The segmentation is then performed by Bayesian decision rule. Segmentation results are presented to prove the effectiveness of different approaches.
U2 - 10.1109/IEMBS.1992.5762067
DO - 10.1109/IEMBS.1992.5762067
M3 - Conference contribution
AN - SCOPUS:0042718393
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1847
EP - 1848
BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992
A2 - Coatrieux, Jean Louis
A2 - Morucci, Jean Pierre
A2 - Laxminarayan, Swamy
A2 - Plonsey, Robert
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992
Y2 - 29 October 1992 through 1 November 1992
ER -