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001-es BibID:	bibEBI00026058
Első szerző:	Kovács György (programtervező matematikus, fizikus)
Cím:	Extraction of vascular system in retina images using averaged one-dependence estimators and orientation estimation in Hidden Markov Random Fields / György Kovács, András Hajdu
Dátum:	2011
ISSN:	1945-7936
Megjegyzések:	The proper segmentation of the vascular system of the retina currently attracts wide interest. As a precious outcome, a successful segmentation may lead to the improvement of automatic screening systems. Namely, the detection of the vessels helps the localization of other anatomical parts and lesions besides the vascular disorders. In this paper, we recommend a novel approach for the segmentation of the vascular system in retina images, based on Hidden Markov Random Fields (HMRF). We extend the optimization problem of HMRF models considering the tangent vector field of the image to enhance the connectivity of the vascular system consisting of elongated structures. To enhance the probability estimation during the solution of the Hidden Markov problem, the Averaged One-Dependence Estimator (AODE) is used instead of the commonly used naive Bayes estimators, since AODE uses a weaker assumption than total independence of features. The advantages of our method is discussed through a quantitative analysis on a publicly available database.
ISBN:	978-1-4244-4128-0
Tárgyszavak:	Természettudományok Matematika- és számítástudományok tanulmány, értekezés vessel veraged one-dependence estimator automatic screening averaged one-dependence estimator elongated structures hidden Markov fields hidden Markov random fields Naive Bayes optimization problems Oorientation estimation probability estimation retina tangent vectors vascular system estimation medical imaging image segmentation
Megjelenés:	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Proceedings. - p. 693-696. -
További szerzők:	Hajdu András (1973-) (matematikus, informatikus)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat Szerző által megadott URL DOI
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