Método automatizado de análisis de imágenes para detección del parásito de la malaria p-vivax en imágenes de gota gruesa
DOI:
https://doi.org/10.18046/syt.v10i20.1151Palavras-chave:
Malaria, Thick film microscopy, redes neuronales, Análisis de Componentes Principales, PCAResumo
El artículo describe un método de análisis de gota gruesa para la detección del parásito de la malaria en la sangre, realizado a partir del análisis de imágenes. Para la etapa de segmentación de las imágenes el método desarrollado combina las técnicas Agnes y del Gradiente Morfológico. La extracción de características se basa en la transformada de Wavelet y es seguida por una etapa de clasificación de la red neuronal. El método utiliza la técnica de Análisis de Componentes Principales (PCA) para reducir el número de funciones y mejorar el rendimiento de la red neuronal. La tasa de detección efectiva (True-Positive rate) lograda fue de 77,19% en la determinación de un parásito específico, y de 76,45% en la determinación de al menos un parásito en una imagen de microscopio.
Referências
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Kim, Y., & Romeike, B. (2006).Automated nuclear segmentation in the determination of the Ki-67 labeling index in meningiomas. Clinical Neuropathology, Vol 25(2), 67-73.
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Romero, E., & Sarmiento, W.J (2004). Automatic detection of malaria parasites in thick blood films stained with Haematoxylin-Eosin (presentado en III Iberian Latin American and Caribbean congress of Medical Physics, ALFIM 2004). Rio de Janeiro, Brazil.
Ruberto, C., Dempster, A., Khan, S., & Jarra, B. (2000). Automatic thresholding of infected blood images using granulometry and regional extrema. In Proceedings, 15th. International Conference on Pattern Recognition, pp.3445-3448.
Ruberto, C., Dempster, A., Khan, S., & Jarra, B. (2002). Analysis of infected blood cell images using morphological operators. Image and Vision Computing, 20(2),133-146.
Sio, S., Sun, W., Kumar, S., Bin, W., Tan, S., Ong, S., Kikuchi, H., Oshima, Y., & Tan, K. (2007). Malaria count: an image analysis-based program for the accurate determination of parasitemia. Journal of Microbiological Methods 68 (1), 11–18.
Tek, F.B., Dempster, A., & Kale, I. (2009, July). Computer vision for microscopy diagnosis of malaria. Malaria Journal, 8. doi:10.1186/1475-2875-8-153
Van der Laan, M. & Pollard, K. (2003). Hybrid clustering of gene expression data with visualization and the bootstrap. Journal of Statistical Planning and Inference, 117(2). 275-303.
Wu, K., Gauthier, D., &Levine, M.D. (1995). Live cell image segmentation. IEEE Transactions on Biomedical Engineering, 42(1), 1-12.
Yang, L., Meer, P., & Foran, D. (2005). Unsupervised segmentation based on robust estimation and color active contour models. IEEE Transactions on Information Techonology in Biomedicine, 9(3), 475-486
Angulo, J. (2003). Morphologie mathématique et indexation d’images couleur. Application à la microscopie en biomédecine (Doctoral Thesis), University of Minas, Paris, Francia.
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679-698
Getz G. & Levine E. (2000). Coupled two-way clustering analysis of gene microarray data. Proceedings of the National Academy of sciences of the United States of America, 97(22), 12079.12084.
Gutiérrez, S., & Arróspide, N. (2003). Manual de procedimientos de laboratorio para el diagnostico de Malaria (Serie de Normas Tecnicas N 39). Lima, Perú: Instituto Nacional de Salud.
Hanscheid, T. (2003). Current strategies to avoid misdiagnosis of malaria. Clinical Microbiology and Infection, 9(6), 497–504.
Iqbal J, Khalid N, & Hira P.R. (2002), Comparison of two commercial assays with expert microscopy for confirmation of symptomatically diagnosed malaria. Journal of Clinical Microbiology, 40(12), 4675-4678
Katz, A. (2000). Image analysis and supervised learning in the automated differentiation of white blood cells from microscopic images (Master´s thesis). RMIT University, Melbourne, Australia.
Kim, Y., & Romeike, B. (2006).Automated nuclear segmentation in the determination of the Ki-67 labeling index in meningiomas. Clinical Neuropathology, Vol 25(2), 67-73.
Le, M.T., Bretschneider, T., Kuss, C & Preiser, P. (2008, march). A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears”, BMC Cell Biology, 9(art.15).
Mens, P., Spieker, N., Omar, S., Heijnen, M., Schallig, H., & Kager P.A. (2007). Is molecular biology the best alternative for diagnosis of malaria to microscopy? A comparison between microscopy, antigen detection and molecular tests in rural Kenya and urban Tanzania. Tropical Medicine & International Health, 12(2), 238-244.
Moody, A (2002). Rapid diagnostic tests for malaria parasites. Clinical Microbiology Reviews 15(1), 66-78.
Murray, C.K., Bell, D., Gasser, R.A., & Wongsrichanalai, C. (2003). Rapid diagnostic testing for malaria. Tropical Medicine & International Health, 8(10): 876–883.
Pinzón, R., Garavito, G., Hata, Y., Arteaga, L., García, J.D. (2004). Desarrollo de un Sistema de Análisis Automático de Imágenes de Extendidos Sanguíneos. En Memorias del Congreso Espanol de la Sociedad de Ingeniería Biomédica, 2004, pp.45-59
Premaratnea, S., Dharshani, N., Shyam, F., Pererab, W., & Rajapakshab, A. (2007). A neural network architecture for automated recognition of intracellular malaria. Retrieved from http://kosmi.snubi.org/2003_fall/APAMI_CJKMI/O3-3-020-Premaratne-0731.pdf
Rao, K (2004). Application of mathematical morphology to biomedical image processing (Ph.D. thesis). Westminster University, London, UK.
Romero, E., & Sarmiento, W.J (2004). Automatic detection of malaria parasites in thick blood films stained with Haematoxylin-Eosin (presentado en III Iberian Latin American and Caribbean congress of Medical Physics, ALFIM 2004). Rio de Janeiro, Brazil.
Ruberto, C., Dempster, A., Khan, S., & Jarra, B. (2000). Automatic thresholding of infected blood images using granulometry and regional extrema. In Proceedings, 15th. International Conference on Pattern Recognition, pp.3445-3448.
Ruberto, C., Dempster, A., Khan, S., & Jarra, B. (2002). Analysis of infected blood cell images using morphological operators. Image and Vision Computing, 20(2),133-146.
Sio, S., Sun, W., Kumar, S., Bin, W., Tan, S., Ong, S., Kikuchi, H., Oshima, Y., & Tan, K. (2007). Malaria count: an image analysis-based program for the accurate determination of parasitemia. Journal of Microbiological Methods 68 (1), 11–18.
Tek, F.B., Dempster, A., & Kale, I. (2009, July). Computer vision for microscopy diagnosis of malaria. Malaria Journal, 8. doi:10.1186/1475-2875-8-153
Van der Laan, M. & Pollard, K. (2003). Hybrid clustering of gene expression data with visualization and the bootstrap. Journal of Statistical Planning and Inference, 117(2). 275-303.
Wu, K., Gauthier, D., &Levine, M.D. (1995). Live cell image segmentation. IEEE Transactions on Biomedical Engineering, 42(1), 1-12.
Yang, L., Meer, P., & Foran, D. (2005). Unsupervised segmentation based on robust estimation and color active contour models. IEEE Transactions on Information Techonology in Biomedicine, 9(3), 475-486
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2012-03-31
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Original Research
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Esta publicação está licenciada sob os termos da licença CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.pt_BR).
