Publication:
Sobel and Canny Edges Segmentations for the Dental Age Assessment

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dc.Conferencecode118343
dc.Conferencedate19 December 2014 through 21 December 2014
dc.Conferencename1st International Conference on Computer Assisted System in Health, CASH 2014
dc.citedby7
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsFaculty of Dentistry
dc.contributor.affiliationsPenang Skills Development Centre
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.affiliationsUniversiti Sains Malaysia (USM)
dc.contributor.authorRazali M.R.M.en_US
dc.contributor.authorAhmad N.S.en_US
dc.contributor.authorHassan R.en_US
dc.contributor.authorZaki Z.M.en_US
dc.contributor.authorIsmail W.en_US
dc.date.accessioned2024-05-28T08:24:52Z
dc.date.available2024-05-28T08:24:52Z
dc.date.issued2015
dc.description.abstractThe x-ray image is a grey scale image and the distribution of the intensity of the pixel is uneven. The x-ray image widely use in dental age assessment especially Demirjian method. The purpose of the dental age assessment is to estimate the age of unidentified bodies. The current process is done manually by the examiner. The process potentially converted to an automated system. The development an automated dental age assessment required segmentation process, which is dividing the image into multiple meaningful parts based on region and edge. The edge segmentation form a contour based on the links detected. The authors present two types of edge segmentation methods (i.e. Sobel and Canny). The objective of the study is to make a comparison between the two methods. Result showed Sobel method was able to segment all the teeth area and remove the noise on the x-ray image while Canny algorithm was not able to segment all the teeth area especially incisors. The region of segmentation is important because one of the requirements in Demirjian method is to assess all the teeth types in quadrant 2 and quadrant 3. Based on the result, the experiment showed the Sobel algorithm able to segment most of the teeth area in quadrant 2 and quadrant 3. � 2014 IEEE.
dc.description.natureFinalen_US
dc.editorZeki A.M.Rahmat R.W.O.K.en_US
dc.identifier.ArtNo7286671
dc.identifier.doi10.1109/CASH.2014.10
dc.identifier.epage66
dc.identifier.isbn9781480000000
dc.identifier.scopus2-s2.0-84962106246
dc.identifier.spage62
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84962106246&doi=10.1109%2fCASH.2014.10&partnerID=40&md5=aa22bf12281ffcb73256369442164384
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8575
dc.languageEnglish
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2014 International Conference on Computer Assisted System in Health, CASH 2014
dc.sourceScopus
dc.subjectCannyen_US
dc.subjectDemirjianen_US
dc.subjectSegmentationen_US
dc.subjectSobelen_US
dc.subjectAutomationen_US
dc.subjectEdge detectionen_US
dc.subjectX ray analysisen_US
dc.subjectAutomated systemsen_US
dc.subjectCannyen_US
dc.subjectCanny algorithmen_US
dc.subjectDemirjianen_US
dc.subjectEdge segmentationen_US
dc.subjectGrey scale imagesen_US
dc.subjectSegmentation processen_US
dc.subjectSobelen_US
dc.subjectImage segmentationen_US
dc.titleSobel and Canny Edges Segmentations for the Dental Age Assessment
dc.typeConference Paperen_US
dspace.entity.typePublication

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