TY - GEN
T1 - Detection of branching in trabecular bone using multiscale COSFIRE filter for osteoporosis identification
AU - Wihandika, Randy Cahya
AU - Arifin, Agus Zainal
AU - Yuniarti, Anny
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/6/25
Y1 - 2018/6/25
N2 - Mandibular bone is among the bones which mineral density is reduced due to osteoporosis. Hence, dental panoramic gradiographs have become an alternative solution to identify osteoporosis. Previous study has shown that the number of branching differs between normal people and patients with osteoporosis. Another study has proposed an algorithm called COSFIRE which is able to detect branches in retinal vessel images. However, branch structures in trabecular bone differ from that in retinal vessel images. For that reason, the COSFIRE method alone is considered unable to detect such structures. In this study, we propose multiscale mechanism to detect different size of trabecular branches. Morphological structures in the trabecular bone is first enhanced using the line operator method. Then the branches are detected using COSFIRE. Experiment of the branching detection conducted to 20 images yields an accuracy of 95.25% whereas the experiment of the classification step gives the sensitivity, specificity, and accuracy of 0.95122, 0.26315, and 0.55102, respectively.
AB - Mandibular bone is among the bones which mineral density is reduced due to osteoporosis. Hence, dental panoramic gradiographs have become an alternative solution to identify osteoporosis. Previous study has shown that the number of branching differs between normal people and patients with osteoporosis. Another study has proposed an algorithm called COSFIRE which is able to detect branches in retinal vessel images. However, branch structures in trabecular bone differ from that in retinal vessel images. For that reason, the COSFIRE method alone is considered unable to detect such structures. In this study, we propose multiscale mechanism to detect different size of trabecular branches. Morphological structures in the trabecular bone is first enhanced using the line operator method. Then the branches are detected using COSFIRE. Experiment of the branching detection conducted to 20 images yields an accuracy of 95.25% whereas the experiment of the classification step gives the sensitivity, specificity, and accuracy of 0.95122, 0.26315, and 0.55102, respectively.
KW - Linear structure
KW - Mandibular bone
KW - Osteoporosis
UR - http://www.scopus.com/inward/record.url?scp=85054862267&partnerID=8YFLogxK
U2 - 10.1145/3233347.3233381
DO - 10.1145/3233347.3233381
M3 - Conference contribution
AN - SCOPUS:85054862267
SN - 9781450364720
T3 - ACM International Conference Proceeding Series
SP - 147
EP - 151
BT - Proceedings of 2018 the 4th International Conference on Frontiers of Educational Technologies, ICFET 2018 - Workshop 2018 3rd International Conference on Knowledge Engineering and Applications, ICKEA 2018
PB - Association for Computing Machinery
T2 - 4th International Conference on Frontiers of Educational Technologies, ICFET 2018, Jointly with its Workshop the 3rd International Conference on Knowledge Engineering and Applications, ICKEA 2018
Y2 - 25 June 2018 through 27 June 2018
ER -