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Journal > Jurnal Ilmu Komputer dan Informasi > PARAMETER SIGMOID TRANSFORM CONTRAST ENHANCEMENT FOR DENTAL RADIOGRAPH CLASSIFICATION AND NUMBERING SYSTEM

 

Jurnal Ilmu Komputer dan Informasi
##issue.vol## 8, ##issue.no## 2 (2015): Jurnal Ilmu Komputer dan Informasi
PARAMETER SIGMOID TRANSFORM CONTRAST ENHANCEMENT FOR DENTAL RADIOGRAPH CLASSIFICATION AND NUMBERING SYSTEM
Andi Baso Kaswar ( Department of Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember Surabaya Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia)
Saprina Mamase ( Department of Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember Surabaya Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia)
Saiful Bahri Musa ( Department of Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember Surabaya Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia)
Ahmad Mustofa Hadi ( Department of Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember Surabaya Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia)
Anny Yuniarti ( Department of Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember Surabaya Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia)
Agus Zainal Arifin ( Department of Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember Surabaya Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia)
Article Info   ABSTRACT
Published date:
27 Aug 2015
 
Dental record is a method that is used to identify a person. The identification process needs a system that could recognize each individual tooth automatically. The similar intensity level between the teeth and the gums is one of the main problem in tooth identification in a dental radiograph. The intensity problem could influence the segmentation process of the system. In this paper, we proposed a new contrast enhancement by using parameter sigmoid transform to increase the segmentation accuracy. There are five main steps in this method. The first step is to fix the contrast of the image with the proposed method. The next steps are to segment the teeth using horizontal and vertical integral projection, feature extraction, and classification using Support Vector Machine (SVM). The last step is teeth numbering. The experiment result using the proposed method have an accuracy rate of 88% for classification and 73% for teeth numbering.
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