Issue

Vol. 7, No. 3, August 2022

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Segmentation of Facial Bones from Skull Point Clouds Based on Smoothed Deviation Angle

https://doi.org/10.22219/kinetik.v7i3.1464
Masy Ari Ulinuha
Universitas Islam Negeri Walisongo
Eko Mulyanto Yuniarno
Institut Teknologi Sepuluh Nopember
I Ketut Eddy Purnama
Institut Teknologi Sepuluh Nopember
Mochamad Hariadi
Institut Teknologi Sepuluh Nopember

Corresponding Author(s) : Masy Ari Ulinuha

ulinuha@walisongo.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 3, August 2022

Abstract

The human skull was the subject of study in various fields. Segmentation could be a basic tool for better understanding the skull. One of the most challenging tasks was facial bone segmentation. Our previous study had succeeded in segmenting facial bones from skull point clouds, however the quality of the results needed to be improved. In this paper, we proposed a new method to improve the results of facial bone segmentation from skull point clouds. The method consists of three stages: deviation angle extraction, smoothing, and thresholding. Each point in the point cloud was assigned a value based on the deviation angle. These values then went through a smoothing process to clarify the differences between the facial bone region and other regions. Next, thresholding was performed to divide the skull into two regions, namely facial bone and non-facial bone. The proposed method had succeeded in improving the quality of the segmentation results by achieving precision=0.931, recall=0.9854, and F=0.9573.

Keywords

Segmentation Facial bone Thresholding Skull Point Cloud Smoothed Deviation Angle
Ulinuha, M. A., Yuniarno, E. M., Purnama, I. K. E., & Hariadi, M. (2022). Segmentation of Facial Bones from Skull Point Clouds Based on Smoothed Deviation Angle. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(3), 251-258. https://doi.org/10.22219/kinetik.v7i3.1464
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. K. M. Day et al., “Advanced Three-Dimensional Technologies in Craniofacial Reconstruction,” Plastic & Reconstructive Surgery, Vol. 148, No. 1, Pp. 94e-108e, Jul. 2021. https://doi.org/10.1097/PRS.0000000000008212
  2. S. A. H. Tabatabaei et al., “Automatic Detection and Monitoring of Abnormal Skull Shape in Children with Deformational Plagiocephaly using Deep Learning,” Scientific Reports, Vol. 11, No. 1, P. 17970, Dec. 2021. https://doi.org/10.1038/s41598-021-96821-7
  3. A. Ari and D. Hanbay, “Deep Learning Based Brain Tumor Classification and Detection System,” Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 26, No. 5, Pp. 2275–2286, Sep. 2018. https://doi.org/10.3906/elk-1801-8
  4. A. Kundu, M. Streed, P. J. Galzi, and A. Johnson, “A Detailed Review of Forensic Facial Reconstruction Techniques,” Medico-Legal Journal, Vol. 89, No. 2, Pp. 106–116, Jun. 2021. https://doi.org/10.1177/0025817221989591
  5. H. J. Kwon, H. Il Koo, J. Park, and N. I. Cho, “Multistage Probabilistic Approach for the Localization of Cephalometric Landmarks,” IEEE Access, Vol. 9, Pp. 21306–21314, 2021. https://doi.org/10.1109/ACCESS.2021.3052460
  6. M. A. Ulinuha, E. M. Yuniarno, M. Hariadi, and I. K. Eddy Purnama, “Extraction of Skull and Face Surfaces from CT Images,” in 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), Pp. 37–40, 2019. https://doi.org/10.1109/ICAIIT.2019.8834469
  7. J. Li et al., “Synthetic Skull Bone Defects for Automatic Patient-specific Craniofacial Implant Design,” Scientific Data, Vol. 8, No. 1, P. 36, Dec. 2021. https://doi.org/10.1038/s41597-021-00806-0
  8. W. Li, G. Chen, H. Yang, R. Chen, and B. Yu, “Learning Point Clouds in EDA,” in Proceedings of the 2021 International Symposium on Physical Design, Pp. 55–62, 2021. https://doi.org/10.1145/3439706.3446895
  9. M. Bassier, M. Vergauwen, and F. Poux, “Point Cloud vs. Mesh Features for Building Interior Classification,” Remote Sensing, Vol. 12, No. 14, P. 2224, Jul. 2020. https://doi.org/10.3390/rs12142224
  10. A. Novo, N. Fariñas-Álvarez, J. Martínez-Sánchez, H. González-Jorge, and H. Lorenzo, “Automatic Processing of Aerial LiDAR Data to Detect Vegetation Continuity in the Surroundings of Roads,” Remote Sensing, Vol. 12, No. 10, P. 1677, May 2020. https://doi.org/10.3390/rs12101677
  11. X. Shen, C. Qin, Y. Du, X. Yu, and R. Zhang, “An Automatic Extraction Algorithm of High Voltage Transmission Lines from Airborne LIDAR Point Cloud Data,” Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 26, No. 4, Pp. 2043–2055, Jul. 2018. https://doi.org/10.3906/elk-1801-23
  12. G. Chen et al., “Pedestrian Detection Based on Panoramic Depth Map Transformed from 3D-LiDAR Data,” Periodica Polytechnica Electrical Engineering and Computer Science, Vol. 64, No. 3, Pp. 274–285, Apr. 2020. https://doi.org/10.3311/PPee.14960
  13. X. Wang, S. Liu, X. Shen, C. Shen, and J. Jia, “Associatively Segmenting Instances and Semantics in Point Clouds,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Pp. 4091–4100, 2019. https://doi.org/10.1109/CVPR.2019.00422
  14. W. Wang, R. Yu, Q. Huang, and U. Neumann, “SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Pp. 2569–2578, 2018. https://doi.org/10.1109/CVPR.2018.00272
  15. A. Fekete and M. Cserep, “Tree Segmentation and Change Detection of Large Urban Areas Based on Airborne LiDAR,” Computers & Geosciences, Vol. 156, P. 104900, Nov. 2021. https://doi.org/10.1016/j.cageo.2021.104900
  16. Y. Xu, W. Yao, S. Tuttas, L. Hoegner, and U. Stilla, “Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11, No. 11, Pp. 4270–4286, Nov. 2018. https://doi.org/10.1109/JSTARS.2018.2817227
  17. C. Choy, J. Gwak, and S. Savarese, “4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Pp. 3070–3079, 2019. https://doi.org/10.1109/CVPR.2019.00319
  18. L. Liu, J. Yu, L. Tan, W. Su, L. Zhao, and W. Tao, “Semantic Segmentation of 3D Point Cloud Based on Spatial Eight-Quadrant Kernel Convolution,” Remote Sensing, Vol. 13, No. 16, P. 3140, Aug. 2021. https://doi.org/10.3390/rs13163140
  19. Y. Xie, J. Tian, and X. X. Zhu, “Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation,” IEEE Geoscience and Remote Sensing Magazine, Vol. 8, No. 4, Pp. 38–59, Dec. 2020. https://doi.org/10.1109/MGRS.2019.2937630
  20. J. R. Jinkins, "Atlas of Neuroradiologic Embryology, Anatomy, and Variants", Philadelphia: Lippincott Williams & Wilkins, 2000.
  21. M. A. Ulinuha, E. M. Yuniarno, I. K. E. Purnama, and M. Hariadi, “Facial Bones Segmentation from Skull Point Clouds Based on Deviation Angle,” Pollack Periodica, Vol. 16, No. 2, Pp. 98–103, Mar. 2021. https://doi.org/10.1556/606.2020.00234
  22. J. Zhang, J.-J. Cao, H.-R. Zhu, D.-M. Yan, and X.-P. Liu, “Geometry Guided Deep Surface Normal Estimation,” Computer-Aided Design, Vol. 142, P. 103119, Jan. 2022. https://doi.org/10.1016/j.cad.2021.103119
  23. Z. Yu, T. Wang, T. Guo, H. Li, and J. Dong, “Robust Point Cloud Normal Estimation via Neighborhood Reconstruction,” Advances in Mechanical Engineering, Vol. 11, No. 4, pp. 1–19, Apr. 2019. https://doi.org/10.1177/1687814019836043
  24. K. Jordan and P. Mordohai, “A Quantitative Evaluation of Surface Normal Estimation in Point Clouds,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Pp. 4220–4226, 2014. https://doi.org/10.1109/IROS.2014.6943157
  25. R. Gonzalez, W. Richard, and E. Steven, Digital Image Processing Using MATLAB, 3rd ed. Knoxville: Gatesmark Publishing, 2020.
  26. M. Y. Iscan and M. Steyn, The Human Skeleton in Forensic Medicine. Springfield, Illinois: Charles C Thomas, 2013.
  27. J. Pont-Tuset and F. Marques, “Supervised Evaluation of Image Segmentation and Object Proposal Techniques,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, No. 7, Pp. 1465–1478, Jul. 2016. https://doi.org/10.1109/TPAMI.2015.2481406
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 10 Download : 9

Downloads

Download data is not yet available.