TY - JOUR
T1 - Detection of overlapping teeth on dental panoramic radiograph
AU - Arifin, Agus Zainal
AU - Adam, Safri
AU - Mohammad, Avin Maulana
AU - Anggris, Fatoni
AU - Indraswari, Rarasmaya
AU - Navastara, Dini Adni
N1 - Publisher Copyright:
© 2019 Intelligent Network and Systems Society.
PY - 2019
Y1 - 2019
N2 - Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.
AB - Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.
KW - Dental panoramic radiograph
KW - Overlapping teeth detection
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85083669410&partnerID=8YFLogxK
U2 - 10.22266/ijies2019.1231.07
DO - 10.22266/ijies2019.1231.07
M3 - Article
AN - SCOPUS:85083669410
SN - 2185-310X
VL - 12
SP - 71
EP - 80
JO - International Journal of Intelligent Engineering and Systems
JF - International Journal of Intelligent Engineering and Systems
IS - 6
ER -