Cellular Nerural Network Approach For Enhancement Of Dental Image With Low Dosed X-Ray

dc.contributor.author	Kilic, Niyazi
dc.contributor.author	Ucan, Osman N.
dc.contributor.author	Ozkarslı, Fatih
dc.contributor.author	Yilmaz, Bulent
dc.contributor.author	Dindar, Seckin
dc.date.accessioned	2015-04-02T06:45:36Z
dc.date.available	2015-04-02T06:45:36Z
dc.date.issued	2007
dc.department	İstanbul Beykent Üniversitesi	en_US
dc.description.abstract	Despite radiography's great importance in dental treatment, they caused serious health problems when were used repeatedly with high amount of radiation. In this paper, we have presented an algorithm for image enhancement in dental image with low dosed x-ray (radiation). We aimed to get high quality images (like images with high dosed x-ray) while we have reduced the amount of radiation in Radio VisioGraphy (RVG), which were used for those patients, by use of Cellular Neural Network (CNN). When compared with input images and CNN output images, CNN outputs better clear and quality than inputs from various angle have been seen.	en_US
dc.identifier.citation	Journal of Science and Technology 1 (2), 2007, 197-206	tr_TR
dc.identifier.issn	1307-3818
dc.language.iso	en	en_US
dc.publisher	Beykent Üniversitesi	tr_TR
dc.relation.publicationcategory	Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı	en_US
dc.subject	Image enhancement	tr_TR
dc.subject	Cellular neural networks	tr_TR
dc.subject	RadioVisioGraphy	tr_TR
dc.subject	X-ray	tr_TR
dc.title	Cellular Nerural Network Approach For Enhancement Of Dental Image With Low Dosed X-Ray	en_US
dc.type	Article	en_US

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