Automatic Ocular Alignment Evaluation for Strabismus Detection Using Index of Phylogenetic Diversity and Support Vector Machine
Resumo
Strabismus is a pathology that affects the parallelism between two eyes. Estimates show that 1.3% to 3.5% of children aged six months to six years have strabismus around the world. Strabismus can lead to irreversible loss of vision, so early detection and appropriate treatment increase the likelihood of alignment being restored to normal. That said, in this study we proposed an automatic evaluation system to detect strabismus in face images, analyzing the horizontal positioning of the limb center in relation to the center of the corners of the eyes. The proposed method consists of five steps: (1) acquisition, (2) detection of the eye region, (3) segmentation and reconstruction of the sclera, (4) detection of the limb region and corners of the eyes and (5) identification of the presence of strabismus, with this approach we obtain 90.1% sensitivity, 100% specificity and 91.1% accuracy.
Palavras-chave:
Strabismus Detection, Hirschberg test, Index of
Phylogenetic Diversity, SegNet
Referências
J. D. S. de Almeida, A. C. Silva, J. A. M. Teixeira, A. C. Paiva, and M. Gattass, Surgical planning for horizontal strabismus using support vector regression, Computers in biology and medicine, vol. 63, pp. 178 - 186, 2015.
T. L. A. Valente, J. D. S. de Almeida, A. C. Silva, J. A. M. Teixeira, and M. Gattass, Automatic diagnosis of strabismus in digital videos through cover test, Computer methods and programs in biomedicine, vol. 140, pp. 295 - 305, 2017.
Y. Zheng, H. Fu, B. Li, and W.-L. Lo, An automatic stimulus and synchronous tracking system for strabismus assessment based on cover test, in 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), vol. IEEE, 2018, pp. 123 - 127.
M. W. Seo, H. K. Yang, J. M. Hwang, and J. -M. Seo, The automated diagnosis of strabismus using an infrared camera, in 6th European Conference of the International Federation for Medical and Biological Engineering. Springer, 2015, pp. 142 - 145.
J. D. Sousa de Almeida, A. C. Silva, J. A. M. Teixeira, A. C. Paiva, and M. Gattass, Computer-aided methodology for syndromic strabismus diagnosis, Journal of Digital Imaging, vol. 28, no. 4, pp. 462 - 473, Aug [Online]. Available: https://doi.org/10.1007/s10278-014-9758-0
H. Proença and L. A. Alexandre, Ubiris: A noisy iris image database, in International Conference on Image Analysis and Processing. Springer, 2005, pp. 970 - 977. DOI: 10.1007/11553595_119
A. Das, U. Pal, M. A. F. Ballester, and M. Blumenstein, Multi-angle based lively sclera biometrics at a distance, in 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM). IEEE, 2014, pp. 22 - 29.
J. Lu, J. Feng, Z. Fan, L. Huang, C. Zheng, and W. Li, Automated strabismus detection based on deep neural networks for telemedicine applications, arXiv preprint arXiv: 02940, 2018.
D. R. Lucio, R. Laroca, E. Severo, A. S. Britto Jr, and D. Menotti, Fully convolutional networks and generative adversarial networks applied to sclera segmentation, CoRR, vol. abs/ 08722, 2018. DOI: 10.1109/btas.2018.8698597
D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, An unsupervised approach for eye sclera segmentation, in Iberoamerican Congress on Pattern Recognition. Springer, 2017, pp. 550 - DOI: 10.1007/978-3-319-75193-1_66
M. De Marsico and D. Riccio, A new data normalization function for multibiometric contexts: A case study, in International Conference Image Analysis and Recognition. Springer, 2008, pp. 1033 - DOI: 10.1007/978-3-540-69812-8_103
A. O. de Carvalho Filho, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, Computer-aided diagnosis of lung nodules in computed tomography by using phylogenetic diversity, genetic algorithm, and svm, Journal of digital imaging, vol. 30, no. 6, pp. 812 - 822, 2017.
--, Lung-nodule classification based on computed tomography using taxonomic diversity indexes and an svm, Journal of Signal Processing Systems, vol. 87, no. 2, pp. 179 - 196, 2017. DOI: 10.1007/s11265-016-1134-5
--, Classification of patterns of benignity and malignancy based on ct using topology-based phylogenetic diversity index and convolutional neural network, Pattern Recognition, vol. 81, pp. 200 - 212, 2018.
F. S. S. de Oliveira, A. O. de Carvalho Filho, A. C. Silva, A. C. de Paiva, and M. Gattass, Classification of breast regions as mass and nonmass based on digital mammograms using taxonomic indexes and svm, Computers in biology and medicine, vol. 57, pp. 42 - 53, 2015.
O. d. S. Santos, T. d. O. Simoes, L. N. Mesquita, A. D. d. Sousa, and A. O. d. Carvalho Filho, Análise filogenética para diferenciacção entre nódulos malignos e benignos, J. health inform, vol. 8, no. supl. I, pp. 1061 - 1070, 2016.
J. Izsák and L. Papp, A link between ecological diversity indices and measures of biodiversity, Ecological Modelling, vol. 130, no. 1-3, pp. 151 - 156, 2000. DOI: 10.1016/s0304-3800(00)00203-9
K. Clarke and R. Warwick, A taxonomic distinctness index and its statistical properties, Journal of applied ecology, vol. 35, no. 4, pp. 523 - 531, 1998. DOI: 10.1046/j.1365-2664.1998.3540523.x
K. R. Clarke, R. Gorley, P. Somerfield, and R. Warwick, Change in marine communities: an approach to statistical analysis and interpretation. Primer-E Ltd, 2014.
G. PIÉNKOWSKI and E. WESTWALEWICZ-MOGILSKA, Trace fossils from the podhale flysch basin, poland-an example of ecologicallybased lithocorrelation, Lethaia, vol. 19, no. 1, pp. 53 - 65, 1986. DOI: 10.1111/j.1502-3931.1986.tb01900.x
R. I. Vane-Wright, C. J. Humphries, and P. H. Williams, What to protect -systematics and the agony of choice, Biological conservation, vol. 55, no. 3, pp. 235 - 254, 1991. DOI: 10.1016/0006-3207(91)90030-d
M. Keith, C. Chimimba, B. Reyers, and A. Van Jaarsveld, Taxonomic and phylogenetic distinctiveness in regional conservation assessments: a case study based on extant south african chiroptera and carnivora, in Animal Conservation forum, vol. 8, no. Cambridge University Press, 2005, pp. 279 - 288.
D. P. Faith, Conservation evaluation and phylogenetic diversity, Biological conservation, vol. 61, no. 1, pp. 1 - 10, DOI: 10.1016/0006-3207(92)91201-3
C. O. Webb, Exploring the phylogenetic structure of ecological communities: an example for rain forest trees, The American Naturalist, vol. 156, no. 2, pp. 145 - 155, 2000. DOI: 10.1086/303378
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770 - 778. DOI: 10.1109/cvpr.2016.90
K. Ehsani, R. Mottaghi, and A. Farhadi, Segan: Segmenting and generating the invisible, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6144 - 6153. DOI: 10.1109/cvpr.2018.00643
S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167
R. O'DUDA, Use of hough transformation to detect lines and curves in picture, CACM, vol. 15, pp. 11 - 15, 1972. DOI: 10.1145/361237.361242
S. Hasebe, H. Ohtsuki, R. Kono, and Y. Nakahira, Biometric confirmation of the hirschberg ratio in strabismic children. Investigative ophthalmology & visual science, vol. 39, no. 13, pp. 2782 - 2785, 1998.
C. Khng and R. H. Osher, Evaluation of the relationship between corneal diameter and lens diameter, Journal of Cataract & Refractive Surgery, vol. 34, no. 3, pp. 475 - 479, DOI: 10.1016/j.jcrs.2007.10.043
G. S. Schwartz, The eye exam: a complete guide. Slack Incorporated, 2006.
L. R. Dice, Measures of the amount of ecologic association between species, Ecology, vol. 26, no. 3, pp. 297 - 302, 1945. DOI: 10.2307/1932409
R. Y. Choi and B. J. Kushner, The accuracy of experienced strabismologists using the hirschberg and krimsky tests, Ophthalmology, vol. 105, no. 7, pp. 1301 - 1306, 1998. DOI: 10.1016/s0161-6420(98)97037-3
T. L. A. Valente, J. D. S. de Almeida, A. C. Silva, J. A. M. Teixeira, and M. Gattass, Automatic diagnosis of strabismus in digital videos through cover test, Computer methods and programs in biomedicine, vol. 140, pp. 295 - 305, 2017.
Y. Zheng, H. Fu, B. Li, and W.-L. Lo, An automatic stimulus and synchronous tracking system for strabismus assessment based on cover test, in 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), vol. IEEE, 2018, pp. 123 - 127.
M. W. Seo, H. K. Yang, J. M. Hwang, and J. -M. Seo, The automated diagnosis of strabismus using an infrared camera, in 6th European Conference of the International Federation for Medical and Biological Engineering. Springer, 2015, pp. 142 - 145.
J. D. Sousa de Almeida, A. C. Silva, J. A. M. Teixeira, A. C. Paiva, and M. Gattass, Computer-aided methodology for syndromic strabismus diagnosis, Journal of Digital Imaging, vol. 28, no. 4, pp. 462 - 473, Aug [Online]. Available: https://doi.org/10.1007/s10278-014-9758-0
H. Proença and L. A. Alexandre, Ubiris: A noisy iris image database, in International Conference on Image Analysis and Processing. Springer, 2005, pp. 970 - 977. DOI: 10.1007/11553595_119
A. Das, U. Pal, M. A. F. Ballester, and M. Blumenstein, Multi-angle based lively sclera biometrics at a distance, in 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM). IEEE, 2014, pp. 22 - 29.
J. Lu, J. Feng, Z. Fan, L. Huang, C. Zheng, and W. Li, Automated strabismus detection based on deep neural networks for telemedicine applications, arXiv preprint arXiv: 02940, 2018.
D. R. Lucio, R. Laroca, E. Severo, A. S. Britto Jr, and D. Menotti, Fully convolutional networks and generative adversarial networks applied to sclera segmentation, CoRR, vol. abs/ 08722, 2018. DOI: 10.1109/btas.2018.8698597
D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, An unsupervised approach for eye sclera segmentation, in Iberoamerican Congress on Pattern Recognition. Springer, 2017, pp. 550 - DOI: 10.1007/978-3-319-75193-1_66
M. De Marsico and D. Riccio, A new data normalization function for multibiometric contexts: A case study, in International Conference Image Analysis and Recognition. Springer, 2008, pp. 1033 - DOI: 10.1007/978-3-540-69812-8_103
A. O. de Carvalho Filho, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, Computer-aided diagnosis of lung nodules in computed tomography by using phylogenetic diversity, genetic algorithm, and svm, Journal of digital imaging, vol. 30, no. 6, pp. 812 - 822, 2017.
--, Lung-nodule classification based on computed tomography using taxonomic diversity indexes and an svm, Journal of Signal Processing Systems, vol. 87, no. 2, pp. 179 - 196, 2017. DOI: 10.1007/s11265-016-1134-5
--, Classification of patterns of benignity and malignancy based on ct using topology-based phylogenetic diversity index and convolutional neural network, Pattern Recognition, vol. 81, pp. 200 - 212, 2018.
F. S. S. de Oliveira, A. O. de Carvalho Filho, A. C. Silva, A. C. de Paiva, and M. Gattass, Classification of breast regions as mass and nonmass based on digital mammograms using taxonomic indexes and svm, Computers in biology and medicine, vol. 57, pp. 42 - 53, 2015.
O. d. S. Santos, T. d. O. Simoes, L. N. Mesquita, A. D. d. Sousa, and A. O. d. Carvalho Filho, Análise filogenética para diferenciacção entre nódulos malignos e benignos, J. health inform, vol. 8, no. supl. I, pp. 1061 - 1070, 2016.
J. Izsák and L. Papp, A link between ecological diversity indices and measures of biodiversity, Ecological Modelling, vol. 130, no. 1-3, pp. 151 - 156, 2000. DOI: 10.1016/s0304-3800(00)00203-9
K. Clarke and R. Warwick, A taxonomic distinctness index and its statistical properties, Journal of applied ecology, vol. 35, no. 4, pp. 523 - 531, 1998. DOI: 10.1046/j.1365-2664.1998.3540523.x
K. R. Clarke, R. Gorley, P. Somerfield, and R. Warwick, Change in marine communities: an approach to statistical analysis and interpretation. Primer-E Ltd, 2014.
G. PIÉNKOWSKI and E. WESTWALEWICZ-MOGILSKA, Trace fossils from the podhale flysch basin, poland-an example of ecologicallybased lithocorrelation, Lethaia, vol. 19, no. 1, pp. 53 - 65, 1986. DOI: 10.1111/j.1502-3931.1986.tb01900.x
R. I. Vane-Wright, C. J. Humphries, and P. H. Williams, What to protect -systematics and the agony of choice, Biological conservation, vol. 55, no. 3, pp. 235 - 254, 1991. DOI: 10.1016/0006-3207(91)90030-d
M. Keith, C. Chimimba, B. Reyers, and A. Van Jaarsveld, Taxonomic and phylogenetic distinctiveness in regional conservation assessments: a case study based on extant south african chiroptera and carnivora, in Animal Conservation forum, vol. 8, no. Cambridge University Press, 2005, pp. 279 - 288.
D. P. Faith, Conservation evaluation and phylogenetic diversity, Biological conservation, vol. 61, no. 1, pp. 1 - 10, DOI: 10.1016/0006-3207(92)91201-3
C. O. Webb, Exploring the phylogenetic structure of ecological communities: an example for rain forest trees, The American Naturalist, vol. 156, no. 2, pp. 145 - 155, 2000. DOI: 10.1086/303378
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770 - 778. DOI: 10.1109/cvpr.2016.90
K. Ehsani, R. Mottaghi, and A. Farhadi, Segan: Segmenting and generating the invisible, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6144 - 6153. DOI: 10.1109/cvpr.2018.00643
S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167
R. O'DUDA, Use of hough transformation to detect lines and curves in picture, CACM, vol. 15, pp. 11 - 15, 1972. DOI: 10.1145/361237.361242
S. Hasebe, H. Ohtsuki, R. Kono, and Y. Nakahira, Biometric confirmation of the hirschberg ratio in strabismic children. Investigative ophthalmology & visual science, vol. 39, no. 13, pp. 2782 - 2785, 1998.
C. Khng and R. H. Osher, Evaluation of the relationship between corneal diameter and lens diameter, Journal of Cataract & Refractive Surgery, vol. 34, no. 3, pp. 475 - 479, DOI: 10.1016/j.jcrs.2007.10.043
G. S. Schwartz, The eye exam: a complete guide. Slack Incorporated, 2006.
L. R. Dice, Measures of the amount of ecologic association between species, Ecology, vol. 26, no. 3, pp. 297 - 302, 1945. DOI: 10.2307/1932409
R. Y. Choi and B. J. Kushner, The accuracy of experienced strabismologists using the hirschberg and krimsky tests, Ophthalmology, vol. 105, no. 7, pp. 1301 - 1306, 1998. DOI: 10.1016/s0161-6420(98)97037-3
Publicado
09/09/2019
Como Citar
SIMÕES, Thayane de O.; SILVA, Aristófanes C.; SOUZA, Johnatan C.; ALMEIDA, João D. S.; PAIVA, Anselmo C..
Automatic Ocular Alignment Evaluation for Strabismus Detection Using Index of Phylogenetic Diversity and Support Vector Machine. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 15. , 2019, São Bernardo do Campo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 73-78.
DOI: https://doi.org/10.5753/wvc.2019.7631.
