An Approach to Classify Chronic Kidney Diseases using Scintigraphy Images
Resumo
Chronic kidney diseases cause over a million deaths worldwide every year. One of the techniques used to diagnose the diseases is renal scintigraphy. However, the way that is processed can vary depending on hospitals and doctors, compromising the reproducibility of the method. In this context, we propose an approach to process the exam using computer vision and machine learning to classify the stage of chronic kidney disease. An analysis of different features extraction methods, such as Gray-Level Co-occurrence Matrix, Structural Co-occurrence Matrix, Local Binary Patters (LBP), Hu's Moments and Zernike's Moments in combination with machine learning methods, such as Bayes, Multi-layer Perceptron, k-Nearest Neighbors, Random Forest and Support Vector Machines (SVM), was performed. The best result was obtained by combining LBP feature extractor with SVM classifier. This combination achieved accuracy of 92.00% and F1-score of 91.00%, indicating that the proposed method is adequate to classify chronic kidney disease in two stages, being a high risk of developing end-stage renal failure and other outcomes, and otherwise.
Referências
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A. Khotanzad and Y. H. Hong, “Invariant image recognition by zernike moments,” IEEE Transactions on pattern analysis and machine intelligence, vol. 12, no. 5, pp. 489–497, 1990. http://doi.org/10.1109/34.55109
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V. N. Vapnik, Statistical Learning Theory. Nova Jersey, EUA: John Wiley & Sons, 1998.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014. https://arxiv.org/abs/1409.1556
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprintarXiv:1704.04861, 2017. https://arxiv.org/abs/1704.04861
C. for Disease Control and Prevention, “National chronic kidney disease fact sheet, 2017,” Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, 2017. https://www.cdc.gov/kidneydisease/pdf/kidney_factsheet.pdf
K. Larmour, A. Maxwell, and A. Courtney, “Improving early detection of chronic kidney disease.” The Practitioner, vol. 259, no. 1779, pp. 19–23, 2015. [link]
K.-U. Eckardt, J. Coresh, O. Devuyst, R. J. Johnson, A. Köttgen, A. S. Levey, and A. Levin, “Evolving importance of kidney disease: from subspecialty to global health burden,” The Lancet, vol. 382, no. 9887, pp. 158–169, 2013. http://doi.org/10.1016/S0140-6736(13)60439-0
A. C. Webster, E. V. Nagler, R. L. Morton, and P. Masson, “Chronic kidney disease,” The Lancet, vol. 389, no. 10075, pp. 1238–1252, 2017. http://doi.org/10.24884/1561-6274-2019-23-5-29-46
A. Taylor Jr and J. Nally, “Clinical applications of renal scintigraphy.” AJR. American journal of roentgenology, vol. 164, no. 1, pp. 31–41, 1995. http://doi.org/10.2214/ajr.164.1.7998566
M. E. Kerl and C. R. Cook, “Glomerular filtration rate and renal scintigraphy,” Clinical techniques in small animal practice, vol. 20, no. 1, pp. 31–38, 2005. http://doi.org/10.1053/j.ctsap.2004.12.005
W. H. S. dos Santos, S. T. M. Ataky, A. C. Silva, A. C. de Paiva, and M. Gattass, “Automatic method for quantitative automatic evaluation in dynamic renal scintilography images,” Multimedia Tools and Applications, vol. 76, no. 18, pp. 19 291–19 315, 2017. http://doi.org/10.1007/s11042-017-4715-9
Y. Qi, P. Hu, Y. Xie, K. Wei, M. Jin, G. Ma, Q. Li, B. Xu, and X. Chen, “Glomerular filtration rate measured by 99mtc-dtpa renal dynamic imaging is significantly lower than that estimated by the ckdepi equation in horseshoe kidney patients,” Nephrology, vol. 21, no. 6, pp. 499–505, 2016. http://doi.org/10.1111/nep.12663
K.-J. Lin, J.-Y. Huang, and Y.-S. Chen, “Fully automatic region of interest selection in glomerular filtration rate estimation from 99mtc-dtpa renogram,” Journal of digital imaging, vol. 24, no. 6, pp. 1010–1023, 2011. http://doi.org/10.1007/s10278-011-9361-6
P. Suapang, K. Dejhan, and S. Yimman, “The estimation of gfr and erpf using adaptive edge-based active contour for the segmentation of structures in dynamic renal scintigraphy,” International Journal of Innovative Computing, Information and Control, vol. 11, no. 1, pp. 87–103, 2015. [link]
Y. Aribi, F. Hamza, W. Ali, A. M. Alimi, and F. Guermazi, “An automated system for the segmentation of dynamic scintigraphic images,” Applied Medical Informatics, vol. 34, no. 2, pp. 1–12, 2014. [link]
“Database of dynamic renal scintigraphy,” [Online; accessed: 12-march-2019]. [Online]. Available: http://www.dynamicrenalstudy.org/
A. S. Levey, J. Coresh, E. Balk, A. T. Kausz, A. Levin, M. W. Steffes, R. J. Hogg, R. D. Perrone, J. Lau, and G. Eknoyan, “National kidney foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification,” Annals of internal medicine, vol. 139, no. 2, pp. 137–147, 2003. http://doi.org/10.7326/0003-4819-139-2-200307150-00013
K. Harris and B. Stribling, “Automated estimated gfr reporting: A new tool to promote safer prescribing in patients with chronic kidney disease?” Therapeutics and clinical risk management, vol. 3, no. 5, p. 969, 2007. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2376075/
R. M. Haralick, K. S. Shanmugam, and I. Dinstein, “Textural features for image classification,” vol. 3, no. 6, 1973, pp. 610–621. https://doi.org/10.1109/TSMC.1973.4309314
T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, 2002. http://doi.org/10.1109/TPAMI.2002.1017623
M.-K. Hu, “Visual pattern recognition by moment invariants,” IRE transactions on information theory, vol. 8, no. 2, pp. 179–187, 1962. https://doi.org/10.1109/TIT.1962.1057692
A. Khotanzad and Y. H. Hong, “Invariant image recognition by zernike moments,” IEEE Transactions on pattern analysis and machine intelligence, vol. 12, no. 5, pp. 489–497, 1990. http://doi.org/10.1109/34.55109
G. L. B. Ramalho et al., “Rotation-invariant feature extraction using a structural co-occurrence matrix,” Measurement, vol. 94, no. 2, pp. 406–415, 2016. http://doi.org/10.1016/j.measurement.2016.08.012
S. Theodoridis and K. Koutroumbas, Pattern Recognition (Fourth Edition). USA: Academic Press, 2008. https://www.elsevier.com/books/pattern-recognition/theodoridis/978-1-59749-272-0
S. Haykin, Neural Networks and Learning Machines. McMaster University, Canada: Prentice Hall, 2008.
K. Fukunaga and P. M. Narendra, “A branch and bound algorithm for computing k-nearest neighbors,” IEEE Transactions on Computers, vol. C-24, no. 7, pp. 750–753, July 1975. http://doi.org/10.1109/T-C.1975.224297
L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001. https://link.springer.com/article/10.1023/A:1010933404324
V. N. Vapnik, Statistical Learning Theory. Nova Jersey, EUA: John Wiley & Sons, 1998.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014. https://arxiv.org/abs/1409.1556
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprintarXiv:1704.04861, 2017. https://arxiv.org/abs/1704.04861
Publicado
28/10/2019
Como Citar
REBOUÇAS FILHO, Pedro Pedrosa; DA SILVA, Suane Pires Pinheiro; ALMEIDA, Jefferson Silva; OHATA, Elene Firmeza; ALVES, Shara Shami Araujo; HERCULES SILVA, Francisco dos Santos.
An Approach to Classify Chronic Kidney Diseases using Scintigraphy Images. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 156-159.
DOI: https://doi.org/10.5753/sibgrapi.est.2019.8318.
