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Tooth Localization with Coarse-to-Fine Auto-Encoders

Yıl 2018, Cilt: 11 Sayı: 1, 29 - 34, 31.01.2018
https://doi.org/10.17671/gazibtd.317893

Öz

 Localization of teeth is a prerequisite task for
most of the computerized methods for dental images such as medical diagnosis
and human identification. Classical deep learning architectures like
convolutional neural networks and auto-encoders seem to work well for tooth
detection, however, it is non-trivial because of the large image size. In this study,
a coarse-to-fine stacked auto-encoder architecture is presented for detection
of teeth in dental panoramic images. The proposed architecture involves
cascaded stacked auto-encoders where sizes of the input patches increase with
the successive steps. Only the detected candidate tooth patches are fed into
the successive layers, thus the irrelevant patches are eliminated. The proposed
architecture decreases the cost of detection process while providing precise
localization. The method is tested and validated on a dataset containing 206
dental panoramic images and the results are promising.

Kaynakça

  • [1] M. Abdel-Mottaleb, O. Nomir, D. Nassar, G. Fahmy, and H. Ammar. Challenges of developing an automated dental identification system. In Circuits and Systems, 2003 IEEE 46th Midwest Symposium on, volume 1, pages 411-414, 2003.
  • [2] D. Frejlichowski and R. Wanat. Extraction of teeth shapes from orthopantomograms for forensic human identi_cation. In P. Real, D. Diaz-Pernil, H. Molina Abril, A. Berciano, and W. Kropatsch, editors, Computer Analysis of Images and Patterns, volume 6855 of Lecture Notes in Computer Science, pages 65-72. Springer Berlin Heidelberg, 2011.
  • [3] S. Guzel, A. B. Oktay, and T. Kadir. Automatic tooth identification in dental panoramic images with atlas-based models. In Proceedings of the International Conference on Pattern Recognition Applications and Methods, Volume 2, Lisbon, Portugal, 10-12 January, 2015., pages 136-141, 2015.
  • [4] B. Hariharan, P. A. Arbel_aez, R. B. Girshick, and J. Malik. Hypercolumns for object segmentation and _ne-grained localization. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pages 447-456, 2015.
  • [5] S. Honari, J. Yosinski, P. Vincent, and C. J. Pal. Recombinator networks: Learning coarse-to-_ne feature aggregation. June 2015.
  • [6] A. K. Jain and H. Chen. Registration of dental atlas to radiographs for human identification. In A. K. Jain and N. K. Ratha, editors, Biometric Technology for Human Identification II, volume 5779 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, pages 292-298, Mar. 2005.
  • [7] A. Katsumata and H. Fujita. Progress of computer-aided detection/diagnosis (cad) in dentistry. Japanese Dental Science Review, 50(3):63 - 68, 2014.
  • [8] P. Lin, Y. Lai, and P. Huang. An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognition, 43(4):1380 - 1392, 2010.
  • [9] P. Lin, Y. Lai, and P. Huang. An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognition, 43(4):1380 - 1392, 2010.
  • [10] P.-L. Lin, P.-W. Huang, Y. S. Cho, and C.-H. Kuo. An automatic and effective tooth isolation method for dental radiographs. Opto-Electronics Review, 21:126-136, 2013.
  • [11] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
  • [12] M. H. Mahoor and M. Abdel-Mottaleb. Classification and numbering of teeth in dental bitewing images. Pattern Recognition, 38(4):577 - 586, 2005.
  • [13] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211-252, 2015.
  • [14] F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
  • [15] H.-C. Shin, M. Orton, D. Collins, S. Doran, and M. Leach. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4d patient data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8):1930-1943, 2013.
  • [16] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1701-1708, June 2014.
  • [17] J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi. Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer istopathology images. IEEE Transactions on Medical Imaging, 35(1):119-130, Jan 2016.
  • [18] A. Yuniarti. Classification and numbering of dental radiographs for an automated human identification system. Telkomnika Telecommunication, Computing, Electronics and Control, 10(1), 2012.
  • [19] J. Zhang, S. Shan, M. Kan, and X. Chen. Coarse-to-fine auto-encoder networks (CFAN) for real-time face alignment. In Computer Vision - ECCV 2014 - 13th European Conference, Proceedings, Part II, pages 1-16, 2014.
  • [20] J. Zhou and M. Abdel-Mottaleb. A content-based system for human identification based on bitewing dental x-ray images. Pattern Recognition, 38(11), 2005.

Büyükten Küçüğe Oto-kodlayıcılar ile dişlerin konumlandırılması*

Yıl 2018, Cilt: 11 Sayı: 1, 29 - 34, 31.01.2018
https://doi.org/10.17671/gazibtd.317893

Öz

Dişlerin
lokalizasyonu, bilgisayar destekli gerçekleştirilen dental görüntülerden insan
kimliklendirme ve medikal tanı için bir önşarttır. Konvolusyonel sinir ağları
ve oto-kodlayıcılar gibi klasik derin öğrenme mimarileri diş tanıma işlemi için
başarılı gözükse de dental görüntülerin çok büyük olması nedeniyle tüm arama
uzayının taranması mümkün gözükmemektedir. Bu çalışmada, büyükten-küçüğe
yığınlanmış bir oto-kodlayıcı yapısı ile dental görüntülerden dişleri tanıyan
bir sistem sunulmuştur. Önerilen mimari, girdi görüntü yamalarının boyutlarının
her kademede arttığı bir kademeli yığınlanmış oto-kodlayıcı yapısı içerir.
İlerdeki katmanlara sadece bulunan aday diş yamaları verilir; böylece alakasız
yamalar elimine edilmiş olur. Önerilen mimari tanıma aşamasındaki maliyeti
düşürmekle beraber hassas konumlandırma imkanı sunar. Geliştirilen metot, 206
dental panoramik görüntü içeren bir veri kümesi üzerinde test edilmiştir ve
sonuçlar ümit vericidir.

Kaynakça

  • [1] M. Abdel-Mottaleb, O. Nomir, D. Nassar, G. Fahmy, and H. Ammar. Challenges of developing an automated dental identification system. In Circuits and Systems, 2003 IEEE 46th Midwest Symposium on, volume 1, pages 411-414, 2003.
  • [2] D. Frejlichowski and R. Wanat. Extraction of teeth shapes from orthopantomograms for forensic human identi_cation. In P. Real, D. Diaz-Pernil, H. Molina Abril, A. Berciano, and W. Kropatsch, editors, Computer Analysis of Images and Patterns, volume 6855 of Lecture Notes in Computer Science, pages 65-72. Springer Berlin Heidelberg, 2011.
  • [3] S. Guzel, A. B. Oktay, and T. Kadir. Automatic tooth identification in dental panoramic images with atlas-based models. In Proceedings of the International Conference on Pattern Recognition Applications and Methods, Volume 2, Lisbon, Portugal, 10-12 January, 2015., pages 136-141, 2015.
  • [4] B. Hariharan, P. A. Arbel_aez, R. B. Girshick, and J. Malik. Hypercolumns for object segmentation and _ne-grained localization. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pages 447-456, 2015.
  • [5] S. Honari, J. Yosinski, P. Vincent, and C. J. Pal. Recombinator networks: Learning coarse-to-_ne feature aggregation. June 2015.
  • [6] A. K. Jain and H. Chen. Registration of dental atlas to radiographs for human identification. In A. K. Jain and N. K. Ratha, editors, Biometric Technology for Human Identification II, volume 5779 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, pages 292-298, Mar. 2005.
  • [7] A. Katsumata and H. Fujita. Progress of computer-aided detection/diagnosis (cad) in dentistry. Japanese Dental Science Review, 50(3):63 - 68, 2014.
  • [8] P. Lin, Y. Lai, and P. Huang. An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognition, 43(4):1380 - 1392, 2010.
  • [9] P. Lin, Y. Lai, and P. Huang. An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognition, 43(4):1380 - 1392, 2010.
  • [10] P.-L. Lin, P.-W. Huang, Y. S. Cho, and C.-H. Kuo. An automatic and effective tooth isolation method for dental radiographs. Opto-Electronics Review, 21:126-136, 2013.
  • [11] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
  • [12] M. H. Mahoor and M. Abdel-Mottaleb. Classification and numbering of teeth in dental bitewing images. Pattern Recognition, 38(4):577 - 586, 2005.
  • [13] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211-252, 2015.
  • [14] F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
  • [15] H.-C. Shin, M. Orton, D. Collins, S. Doran, and M. Leach. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4d patient data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8):1930-1943, 2013.
  • [16] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1701-1708, June 2014.
  • [17] J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi. Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer istopathology images. IEEE Transactions on Medical Imaging, 35(1):119-130, Jan 2016.
  • [18] A. Yuniarti. Classification and numbering of dental radiographs for an automated human identification system. Telkomnika Telecommunication, Computing, Electronics and Control, 10(1), 2012.
  • [19] J. Zhang, S. Shan, M. Kan, and X. Chen. Coarse-to-fine auto-encoder networks (CFAN) for real-time face alignment. In Computer Vision - ECCV 2014 - 13th European Conference, Proceedings, Part II, pages 1-16, 2014.
  • [20] J. Zhou and M. Abdel-Mottaleb. A content-based system for human identification based on bitewing dental x-ray images. Pattern Recognition, 38(11), 2005.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ayşe Betül Oktay

Yayımlanma Tarihi 31 Ocak 2018
Gönderilme Tarihi 31 Mayıs 2017
Yayımlandığı Sayı Yıl 2018 Cilt: 11 Sayı: 1

Kaynak Göster

APA Oktay, A. B. (2018). Tooth Localization with Coarse-to-Fine Auto-Encoders. Bilişim Teknolojileri Dergisi, 11(1), 29-34. https://doi.org/10.17671/gazibtd.317893