Automated Image Analysis Method for p-vivax Malaria Parasite Detection in Thick Film Blood Images

Leonardo Yunda, Andrés Alarcón, Jorge Millán

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.

Keywords


Malaria; Thick film microscopy; Neuronal networks; principal component analysis.

References


Angulo, J. & Serra, J. (2005). Segmentación de imágenes en color utilizando histogramas bi-variables en espacios de color polares luminancia/saturación/matiz. Paris, France: Centro de Morfología Matemática.

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




DOI: http://dx.doi.org/10.18046/syt.v10i20.1151

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