Classification and Numbering of Dental Radiographs for an Automated Human Identification System
Dental based human identification is commonly used in forensic. In a case of large scale investigation, manual identification needs a large amount of time. In this paper, we developed an automated human identification system based on dental radiographs. The system developed has two main stages. The first stage is to arrange a database consisting of labeled dental radiographs. The second stage is the searching process in the database in order to retrieve the identification result. Both stages use a number of image processing techniques, classification methods, and a numbering system in order to generate dental radiograph’s features and patterns. The first technique is preprocessing which includes image enhancement and binarization, single tooth extraction, and feature extraction. Next, we performed dental classification process which aims to classify the extracted tooth into molar or premolar using the binary support vector machine method. After that, a numbering process is executed in accordance with molar and premolar pattern obtained in the previous process. Our experiments using 16 dental radiographs that consist of 6 bitewing radiographs and 10 panoramic radiographs, 119 teeth objects in total, has shown good performance of classification. The accuracy value of dental pattern classification and dental numbering system are 91.6 % and 81.5% respectively.
Muntasa A, Sirajudin IA, Purnomo MH. Appearance Global and Local Structure Fusion for Face Image Recognition. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2011; 9(1): 125-132.
Putra IKGD, Erdiawan. High Performance Palmprint Identification System Based On Two Dimensional Gabor. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2010; 8(3): 309-318.
Lin PL, Lai YH, Huang PW. An Effective Classification and Numbering System for Dental Bitewing Radiographs using Teeth Region and Contour Information. Pattern Recognition Journal. 2010; 43: 1380-1392.
Mahoor MH, Abdel-Mottaleb M. Classification and Numbering of Teeth in Dental Bitewing Images. Pattern Recognition Journal. 2005; 38: 577-586.
Abdel-Mottaleb M., Nomir O, Nasser DE, Fahmy G, Ammar H. Challenges of Developing an Automated Dental Identification System. 64th IEEE Midwest Symposium on Circuits and Systems. Cairo, Egypt. 2004.
Ammar H, Abdel-Mottaleb M, Jain A. Automated Dental Identification System (ADIS). 8th Annual International Conference on Digital Government Research: Bridging Discipline & Domains. Philadelphia, Pennsylvania, USA. 2007; 228: 248-249.
Samopa F. Tooth Shape Measurement on Dental Radiographs for Forensic Personal Identification. Dissertation of Department of Information Engineering, Graduate School of Engineering, Hiroshima University. Hiroshima, Japan; 2009.
Gonzalez RC, Woods RE. Digital Image Processing Using MATLAB. New Jersey, USA: Pearson Prentice-Hall, Pearson Education, Inc. 2004.
Hermawati FA, Koesdijarto R. A Real-Time License Plate Detection System for Parking Access. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2010; 8(2): 97-106.
Duda RO, Hart PE, Stork DG. Pattern Classification Second Edition. New York, USA: Wiley-Interscience. 2001.
Smith TF, Waterman MS. Identification of common molecular subsequences. Journal of Molecular Biology. 1981; 147: 195–197.
Article MetricsAbstract view : 315 times
PDF - 274 times
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.