Classification and Numbering on Posterior Dental Radiography using Support Vector Machine with Mesiodistal Neck Detection
Dental radiography meets challenge to classify the dents into the proper class which useful for forensic and biomedical application. This paper proposed a novel method of classification and numbering on posterior dental radiography using support vector machine (SVM) with mesiodistal neck detection...
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| Main Authors: | , , , , , |
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| Format: | Article PeerReviewed |
| Language: | English English Indonesian English |
| Published: |
Lund University
2021
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| Subjects: | |
| Online Access: | https://repository.unair.ac.id/107772/1/Artikel%204.%20Classification%20and%20Numbering%20on%20Posterior%20Dental%20Radiography%20usingh%20Histogram%20Intersection..pdf https://repository.unair.ac.id/107772/5/23.%206%25%20Classification%20and%20Numbering%20on%20Posterior%20Dental%20Radiography%20using%20Support%20Vector%20Machine%20with%20Mesiodistal%20Neck%20Detection.pdf https://repository.unair.ac.id/107772/6/23.%20Classification%20and%20Numbering%20on%20Posterior%20Dental%20Radiography%20usingh%20Histogram%20Intersection.pdf https://repository.unair.ac.id/107772/9/37.%20Classification%20and%20Numbering.pdf https://repository.unair.ac.id/107772/ |
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| Institution: | Universitas Airlangga |
| Language: | English English Indonesian English |
| Summary: | Dental radiography meets challenge to classify the
dents into the proper class which useful for forensic and
biomedical application. This paper proposed a novel method of classification and numbering on posterior dental radiography using support vector machine (SVM) with mesiodistal neck detection. In this method we developed SVM using a nouvelle feature with mesiodistal neck teeth. This feature was used to solve the problem in the dental image which suffered with completeness of whole part of teeth (crown – root). Preprocessing
for enhancements included morphological operation, contrast adaptive, and tresholding.. Every tooth has been assigned according to universal dental numbering and classified as their sequence order. Our system achieved classification precision of 90 %. This approach is robust and optimal for solving the problem of dental classification |
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