TY - GEN
T1 - 3D region merging for segmentation of teeth on cone-beam computed tomography images
AU - Indraswari, Rarasmaya
AU - Kurita, Takio
AU - Arifin, Agus Zainal
AU - Suciati, Nanik
AU - Astuti, Eha Renwi
AU - Navastara, Dini Adni
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Segmentation of teeth in Cone-Beam Computed Tomography (CBCT) images is challenging problem due to its noise and the similar grayscale intensity of bone and teeth element. In this paper we proposed a new method based on three-dimensional (3D) region merging and histogram thresholding for automatic segmentation of teeth on CBCT images. The proposed 3D region merging algorithm can recognized the teeth element that have similar intensity with the bone element based on the three-dimensional (3D) information of the neighboring slices of the CBCT image. Merging the teeth region will lead to more homogenous grayscale intensity distribution inside the teeth. Then histogram thresholding that utilized the characteristic of CBCT images is performed to binarize the grayscale images and obtain the teeth object. The average accuracy, sensitivity, and specificity of the proposed method are 97.75%, 80.22%, and 98.31%, respectively. The proposed method is fully automatic, therefore lead to more objective and reproducible results.
AB - Segmentation of teeth in Cone-Beam Computed Tomography (CBCT) images is challenging problem due to its noise and the similar grayscale intensity of bone and teeth element. In this paper we proposed a new method based on three-dimensional (3D) region merging and histogram thresholding for automatic segmentation of teeth on CBCT images. The proposed 3D region merging algorithm can recognized the teeth element that have similar intensity with the bone element based on the three-dimensional (3D) information of the neighboring slices of the CBCT image. Merging the teeth region will lead to more homogenous grayscale intensity distribution inside the teeth. Then histogram thresholding that utilized the characteristic of CBCT images is performed to binarize the grayscale images and obtain the teeth object. The average accuracy, sensitivity, and specificity of the proposed method are 97.75%, 80.22%, and 98.31%, respectively. The proposed method is fully automatic, therefore lead to more objective and reproducible results.
KW - Cone-beam computed tomography
KW - Region merging
KW - Teeth segmentation
KW - Three-dimensional image
UR - http://www.scopus.com/inward/record.url?scp=85067122070&partnerID=8YFLogxK
U2 - 10.1109/SCIS-ISIS.2018.00065
DO - 10.1109/SCIS-ISIS.2018.00065
M3 - Conference contribution
AN - SCOPUS:85067122070
T3 - Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
SP - 341
EP - 345
BT - Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
Y2 - 5 December 2018 through 8 December 2018
ER -