Atlas of neonatal face images using triangular Meshes
Resumo
Over the last years, neonatal face analysis has allowed the investigation and creation of non-invasive methods that enable classification of painful stimulus in newborns. In this context, changes on facial movements and expression have provided relevant scientific information and clinical significance, since they describe the presence of the pain itself perceived by the newborns. In this work, we propose and implement a computational framework that uses triangular meshes, with the goal of generating high resolution spatially normalized atlases potentially useful for automatic neonatal pain assessment.
Palavras-chave:
registration, atlas, pain
Referências
K. J. Anand and K. D. Craig, New perspectives on the definition of pain, Pain-Journal of the International Association for the Study of Pain, vol. 67, no. 1, pp. 3 - 6.
Luda, A. G. Nackley, I. E. Tchivileva, S. A. Shabalina, and W. Maixner, Genetic architecture of human pain perception, TRENDS in Genetics, vol. 23, no. 12, pp. 605 - 613, DOI: 10.1016/j.tig.2007.09.004
M. Zimmermann, Geschichte der schmerztherapie 1500 bis 1900, Der Schmerz, vol. 21, no. 4, pp. 297 - 306, DOI: 10.1007/s00482-007-0573-0
R. Guinsburg and M. Cuenca, A linguagem da dor no recém-nascido. documento científico do departamento de neonatologia sociedade brasileira de pediatria: São Paulo, 8 de outubro de 2010 [cited 2014 oct 20].
R. Guinsburg, Avaliacção e tratamento da dor no recém-nascido, J Pediatr (Rio J), vol. 75, no. 3, pp. 149 - 60.
A. G. Chermont, R. Guinsburg, R. d. C. X. Balda, and B. I. Kopelman, O que os pediatras conhecem sobre avaliacção e tratamento da dor no recém-nascido Jornal de Pediatria.
R. E. Grunau, Neonatal pain in very preterm infants: long-term effects on brain, neurodevelopment and pain reactivity, Rambam Maimonides medical journal, vol. 4, no. 4, DOI: 10.5041/rmmj.10132
T. M. Heiderich, A. T. F. S. Leslie, and R. Guinsburg, Neonatal procedural pain can be assessed by computer software that has good sensitivity and specificity to detect facial movements, Acta Paediatrica, vol. 104, no. 2, pp. e63 - e69, DOI: 10.1111/apa.12861
S. Brahnam, C.-F. Chuang, F. Y. Shih, and M. R. Slack, Machine recognition and representation of neonatal facial displays of acute pain, Artificial intelligence in medicine, vol. 36, no. 3, pp. 211 - 222, DOI: 10.1016/j.artmed.2004.12.003
S. Brahnam, C.-F. Chuang, R. S. Sexton, and F. Y. Shih, Machine assessment of neonatal facial expressions of acute pain, Decision Support Systems, vol. 43, no. 4, pp. 1242 - 1254, DOI: 10.1016/j.dss.2006.02.004
B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Relevance vector machine learning for neonate pain intensity assessment using digital imaging, IEEE Transactions on biomedical engineering, vol. 57, no. 6, pp. 1457 - 1466.
L. Nanni, S. Brahnam, and A. Lumini, A local approach based on a local binary patterns variant texture descriptor for classifying pain states, Expert Systems with Applications, vol. 37, no. 12, pp. 7888 - 7894, DOI: 10.1016/j.eswa.2010.04.048
M. N. Mansor and M. N. Rejab, Infant pain recognition system with glcm features and gann under unstructed lighting condition, in Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on. IEEE, 2013, pp. 243 - 248.
G. Lu, C. Yang, M. Chen, and X. Li, Sparse representation based facial expression classification for pain assessment in neonates, in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016 12th International Conference on. IEEE, 2016, pp. 1615 - 1619.
G. Zamzmi, D. Goldgof, R. Kasturi, and Y. Sun, Neonatal pain expression recognition using transfer learning, arXiv preprint arXiv: 01631, 2018.
R. Zhi, G. Zamzmi, D. Goldgof, T. Ashmeade, and Y. Sun, Automatic infants' pain assessment by dynamic facial representation: Effects of profile view, gestational age, gender, and race, Journal of clinical medicine, vol. 7, no. 7, p. 173, DOI: 10.3390/jcm7070173
R. V. Grunau and K. D. Craig, Pain expression in neonates: facial action and cry, Pain, vol. 28, no. 3, pp. 395 - 410, DOI: 10.1016/0304-3959(87)90073-x
T. Hadjistavropoulos, K. Herr, K. M. Prkachin, K. D. Craig, S. J. Gibson, A. Lukas, and J. H. Smith, Pain assessment in elderly adults with dementia, The Lancet Neurology, vol. 13, no. 12, pp. 1216 - 1227, DOI: 10.1016/s1474-4422(14)70103-6
S. J. Gibson, Eigenfit: A statistical learning approach to facial composites, Ph.D. dissertation, University of Kent, 2006.
I. Xavier, M. Pereira, G. Giraldi, S. Gibson, C. Solomon, D. Rueckert, D. Gillies, and C. Thomaz, A photo-realistic generator of most expressive and discriminant changes in 2d face images, in 2015 Sixth International Conference on Emerging Security Technologies (EST). IEEE, 2015, pp. 80 - 85.
I. Xavier, G. Giraldi, S. Gibson, G. Gattas, D. Rueckert, and C. Thomaz, Construction of a spatio-temporal face atlas: Experiments using down syndrome samples, in Conference on Graphics, Patterns and Images (SIBGRAPI): Workshop on Face Processing Applications, 2016, pp. 1 - 4.
D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, Nonrigid registration using free-form deformations: application to breast mr images, IEEE transactions on medical imaging, vol. 18, no. 8, pp. 712 - 721, DOI: 10.1109/42.796284
A. Serag, P. Aljabar, G. Ball, S. J. Counsell, J. P. Boardman, M. A. Rutherford, A. D. Edwards, J. V. Hajnal, and D. Rueckert, Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression, NeuroImage, vol. 59, no. 3, pp. 2255 - 2265, 2012.
P. Aljabar, K. K. Bhatia, M. Murgasova, J. V. Hajnal, J. P. Boardman, L. Srinivasan, M. A. Rutherford, L. Dyet, A. D. Edwards, and D. Rueckert, Assessment of brain growth in early childhood using deformationbased morphometry, Neuroimage, vol. 39, no. 1, pp. 348 - 358, DOI: 10.1016/j.neuroimage.2007.07.067
J. R. Shaffer, E. Orlova, M. K. Lee, E. J. Leslie, Z. D. Raffensperger, C. L. Heike, M. L. Cunningham, J. T. Hecht, C. H. Kau, N. L. Nidey et al., Genome-wide association study reveals multiple loci influencing normal human facial morphology, PLoS genetics, vol. 12, no. 8, p. e1006149
A. Bulat and G. Tzimiropoulos, How far are we from solving the 2d & 3d face alignment problem (and a dataset of 230,000 3d facial landmarks), in International Conference on Computer Vision, DOI: 10.1109/iccv.2017.116
S. Zhang, X. Zhu, Z. Lei, H. Shi, X. Wang, and S. Z. Li, S3fd: Single shot scale-invariant face detector, in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 192 - DOI: 10.1109/iccv.2017.30
S. Umeyama, Least-squares estimation of transformation parameters between two point patterns, IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 4, pp. 376 - 380, DOI: 10.1109/34.88573
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, Deepface: Closing the gap to human-level performance in face verification, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1701 - 1708.
C. B. Barber, D. P. Dobkin, and H. Huhdanpaa, The quickhull algorithm for convex hulls, ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, vol. 22, no. 4, pp. 469 - 483, DOI: 10.1145/235815.235821
J. A. Maintz and M. A. Viergever, A survey of medical image registration, Medical image analysis, vol. 2, no. 1, pp. 1 - 36, DOI: 10.1016/s1361-8415(01)80026-8
Luda, A. G. Nackley, I. E. Tchivileva, S. A. Shabalina, and W. Maixner, Genetic architecture of human pain perception, TRENDS in Genetics, vol. 23, no. 12, pp. 605 - 613, DOI: 10.1016/j.tig.2007.09.004
M. Zimmermann, Geschichte der schmerztherapie 1500 bis 1900, Der Schmerz, vol. 21, no. 4, pp. 297 - 306, DOI: 10.1007/s00482-007-0573-0
R. Guinsburg and M. Cuenca, A linguagem da dor no recém-nascido. documento científico do departamento de neonatologia sociedade brasileira de pediatria: São Paulo, 8 de outubro de 2010 [cited 2014 oct 20].
R. Guinsburg, Avaliacção e tratamento da dor no recém-nascido, J Pediatr (Rio J), vol. 75, no. 3, pp. 149 - 60.
A. G. Chermont, R. Guinsburg, R. d. C. X. Balda, and B. I. Kopelman, O que os pediatras conhecem sobre avaliacção e tratamento da dor no recém-nascido Jornal de Pediatria.
R. E. Grunau, Neonatal pain in very preterm infants: long-term effects on brain, neurodevelopment and pain reactivity, Rambam Maimonides medical journal, vol. 4, no. 4, DOI: 10.5041/rmmj.10132
T. M. Heiderich, A. T. F. S. Leslie, and R. Guinsburg, Neonatal procedural pain can be assessed by computer software that has good sensitivity and specificity to detect facial movements, Acta Paediatrica, vol. 104, no. 2, pp. e63 - e69, DOI: 10.1111/apa.12861
S. Brahnam, C.-F. Chuang, F. Y. Shih, and M. R. Slack, Machine recognition and representation of neonatal facial displays of acute pain, Artificial intelligence in medicine, vol. 36, no. 3, pp. 211 - 222, DOI: 10.1016/j.artmed.2004.12.003
S. Brahnam, C.-F. Chuang, R. S. Sexton, and F. Y. Shih, Machine assessment of neonatal facial expressions of acute pain, Decision Support Systems, vol. 43, no. 4, pp. 1242 - 1254, DOI: 10.1016/j.dss.2006.02.004
B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Relevance vector machine learning for neonate pain intensity assessment using digital imaging, IEEE Transactions on biomedical engineering, vol. 57, no. 6, pp. 1457 - 1466.
L. Nanni, S. Brahnam, and A. Lumini, A local approach based on a local binary patterns variant texture descriptor for classifying pain states, Expert Systems with Applications, vol. 37, no. 12, pp. 7888 - 7894, DOI: 10.1016/j.eswa.2010.04.048
M. N. Mansor and M. N. Rejab, Infant pain recognition system with glcm features and gann under unstructed lighting condition, in Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on. IEEE, 2013, pp. 243 - 248.
G. Lu, C. Yang, M. Chen, and X. Li, Sparse representation based facial expression classification for pain assessment in neonates, in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016 12th International Conference on. IEEE, 2016, pp. 1615 - 1619.
G. Zamzmi, D. Goldgof, R. Kasturi, and Y. Sun, Neonatal pain expression recognition using transfer learning, arXiv preprint arXiv: 01631, 2018.
R. Zhi, G. Zamzmi, D. Goldgof, T. Ashmeade, and Y. Sun, Automatic infants' pain assessment by dynamic facial representation: Effects of profile view, gestational age, gender, and race, Journal of clinical medicine, vol. 7, no. 7, p. 173, DOI: 10.3390/jcm7070173
R. V. Grunau and K. D. Craig, Pain expression in neonates: facial action and cry, Pain, vol. 28, no. 3, pp. 395 - 410, DOI: 10.1016/0304-3959(87)90073-x
T. Hadjistavropoulos, K. Herr, K. M. Prkachin, K. D. Craig, S. J. Gibson, A. Lukas, and J. H. Smith, Pain assessment in elderly adults with dementia, The Lancet Neurology, vol. 13, no. 12, pp. 1216 - 1227, DOI: 10.1016/s1474-4422(14)70103-6
S. J. Gibson, Eigenfit: A statistical learning approach to facial composites, Ph.D. dissertation, University of Kent, 2006.
I. Xavier, M. Pereira, G. Giraldi, S. Gibson, C. Solomon, D. Rueckert, D. Gillies, and C. Thomaz, A photo-realistic generator of most expressive and discriminant changes in 2d face images, in 2015 Sixth International Conference on Emerging Security Technologies (EST). IEEE, 2015, pp. 80 - 85.
I. Xavier, G. Giraldi, S. Gibson, G. Gattas, D. Rueckert, and C. Thomaz, Construction of a spatio-temporal face atlas: Experiments using down syndrome samples, in Conference on Graphics, Patterns and Images (SIBGRAPI): Workshop on Face Processing Applications, 2016, pp. 1 - 4.
D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, Nonrigid registration using free-form deformations: application to breast mr images, IEEE transactions on medical imaging, vol. 18, no. 8, pp. 712 - 721, DOI: 10.1109/42.796284
A. Serag, P. Aljabar, G. Ball, S. J. Counsell, J. P. Boardman, M. A. Rutherford, A. D. Edwards, J. V. Hajnal, and D. Rueckert, Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression, NeuroImage, vol. 59, no. 3, pp. 2255 - 2265, 2012.
P. Aljabar, K. K. Bhatia, M. Murgasova, J. V. Hajnal, J. P. Boardman, L. Srinivasan, M. A. Rutherford, L. Dyet, A. D. Edwards, and D. Rueckert, Assessment of brain growth in early childhood using deformationbased morphometry, Neuroimage, vol. 39, no. 1, pp. 348 - 358, DOI: 10.1016/j.neuroimage.2007.07.067
J. R. Shaffer, E. Orlova, M. K. Lee, E. J. Leslie, Z. D. Raffensperger, C. L. Heike, M. L. Cunningham, J. T. Hecht, C. H. Kau, N. L. Nidey et al., Genome-wide association study reveals multiple loci influencing normal human facial morphology, PLoS genetics, vol. 12, no. 8, p. e1006149
A. Bulat and G. Tzimiropoulos, How far are we from solving the 2d & 3d face alignment problem (and a dataset of 230,000 3d facial landmarks), in International Conference on Computer Vision, DOI: 10.1109/iccv.2017.116
S. Zhang, X. Zhu, Z. Lei, H. Shi, X. Wang, and S. Z. Li, S3fd: Single shot scale-invariant face detector, in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 192 - DOI: 10.1109/iccv.2017.30
S. Umeyama, Least-squares estimation of transformation parameters between two point patterns, IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 4, pp. 376 - 380, DOI: 10.1109/34.88573
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, Deepface: Closing the gap to human-level performance in face verification, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1701 - 1708.
C. B. Barber, D. P. Dobkin, and H. Huhdanpaa, The quickhull algorithm for convex hulls, ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, vol. 22, no. 4, pp. 469 - 483, DOI: 10.1145/235815.235821
J. A. Maintz and M. A. Viergever, A survey of medical image registration, Medical image analysis, vol. 2, no. 1, pp. 1 - 36, DOI: 10.1016/s1361-8415(01)80026-8
Publicado
09/09/2019
Como Citar
ORONA, Pedro A. S. S.; FABBRO, Davi A. D.; HEIDERICH, Tatiany M.; BARROS, Marina C. M.; BALDA, Rita C. X.; GUINSBURG, Ruth; THOMAZ, Carlos E..
Atlas of neonatal face images using triangular Meshes. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 15. , 2019, São Bernardo do Campo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 19-24.
DOI: https://doi.org/10.5753/wvc.2019.7622.
