Automated Image Analysis Method for p-vivax Malaria Parasite Detection in Thick Film Blood Images
DOI:
https://doi.org/10.18046/syt.v10i20.1151Keywords:
Malaria, Thick film microscopy, Neuronal networks, principal component analysis.Abstract
An image analysis method for Malaria parasite detection in thick film blood images is described. The developed method uses a combination of AGNES and Morphological Gradient techniques in the image segmentation stage. Wavelet-based feature extraction is followed by a neural network classification stage. Principal Component Analysis (PCA) is used to reduce the number of features and improve the performance of the neuronal network. The true positive rate for determining a specific parasite was of 77.19%, while a 76.45% was obtained in determining at least a parasite in a microscopy image.References
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Wu, K., Gauthier, D., &Levine, M.D. (1995). Live cell image segmentation. IEEE Transactions on Biomedical Engineering, 42(1), 1-12.
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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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