TY - GEN
T1 - Teeth segmentation on dental panoramic radiographs using decimation-free directional filter bank thresholding and multistage adaptive thresholding
AU - Indraswari, Rarasmaya
AU - Arifin, Agus Zainal
AU - Navastara, Dini Adni
AU - Jawas, Naser
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/12
Y1 - 2016/1/12
N2 - Dental utilization typically associated with tooth shape features which are extracted from dental panoramic radiograph image. However, because dental panoramic radiograph images usually have low contrast, we need a segmentation method that can work well on low contrast images and make the tooth shape is evident. In this paper, we propose a system to do teeth segmentation using Decimation-Free Directional Filter Bank Thresholding (DDFBT) and Multistage Adaptive Thresholding (MAT). The system is built with three main steps, which are formation of vertical and horizontal directional images using DDFBT, enhancement on directional images for teeth edge reinforcement and noise removal, and segmentation using MAT with Sauvola Local Thresholding. The experimental result on 40 teeth images shows that this system has a better performance than Otsu Thresholding, Sauvola Local Thresholding, and MAT with Niblack Local Thresholding with misclassification error (ME) and relative foreground area error (RAE) values are 17.0% and 9.7%.
AB - Dental utilization typically associated with tooth shape features which are extracted from dental panoramic radiograph image. However, because dental panoramic radiograph images usually have low contrast, we need a segmentation method that can work well on low contrast images and make the tooth shape is evident. In this paper, we propose a system to do teeth segmentation using Decimation-Free Directional Filter Bank Thresholding (DDFBT) and Multistage Adaptive Thresholding (MAT). The system is built with three main steps, which are formation of vertical and horizontal directional images using DDFBT, enhancement on directional images for teeth edge reinforcement and noise removal, and segmentation using MAT with Sauvola Local Thresholding. The experimental result on 40 teeth images shows that this system has a better performance than Otsu Thresholding, Sauvola Local Thresholding, and MAT with Niblack Local Thresholding with misclassification error (ME) and relative foreground area error (RAE) values are 17.0% and 9.7%.
KW - Decimation-Free Directional Filter Bank Thresholding
KW - Multistage Adaptive Thresholding
KW - Sauvola Local Thresholding
KW - dental panoramic radiograph
KW - directional filtering
UR - http://www.scopus.com/inward/record.url?scp=84964933281&partnerID=8YFLogxK
U2 - 10.1109/ICTS.2015.7379870
DO - 10.1109/ICTS.2015.7379870
M3 - Conference contribution
AN - SCOPUS:84964933281
T3 - Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015
SP - 49
EP - 54
BT - Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Information and Communication Technology and Systems, ICTS 2015
Y2 - 16 September 2015
ER -