NDLI: Automated Status Identification of Microscopic Images Obtained from Malaria Thin Blood Smears

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Automated Status Identification of Microscopic Images Obtained from Malaria Thin Blood Smears

Content Provider	CiteSeerX
Author	Isk, Aulia Arif Anggraini, Dian Pratama, Christian Hartono, Reggio Nurtanio Rozi, Ismail Ekoprayitno Nugroho, Anto Satriyo
Abstract	Abstract — Development of an accurate laboratory diagnostic tool, as recommended by WHO, is the key step to overcome the serious global health burden caused by malaria. This study aims to explore the possibility of computerized diagnosis of malaria and to develop a novel image processing algorithm to reliably detect the presence of malaria parasite from Plasmodium falciparum species in thin smears of Giemsa stained peripheral blood sample. The algorithm was designed as an expert system based on the method used by medical practitioner performing microscopy diagnosis of malaria. Digital images were acquired using a digital camera connected to a light microscope. Prior to processing, the images were subjected to gray-scale conversion to decrease image variability. Global thresholding were implemented to obtain erythrocyte and other blood cell components in each image. The segmented images were further processed to obtain possibly infected erythrocyte and the components of parasite inside the corresponding erythrocyte using multiple threshold. These parasite’s constituents (nucleus and cytoplasm) were used as the preliminary basis for parasite/non parasite classification. Malaria samples prepared and provided by Eijkman Institute of Molecular Biology Indonesia were used to test the proposed algorithm.
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Subject Keyword	Blood Cell Component Gray-scale Conversion Computerized Diagnosis Digital Image Peripheral Blood Sample Corresponding Erythrocyte Malaria Sample Molecular Biology Indonesia Expert System Preliminary Basis Malaria Thin Blood Smear Parasite Constituent Eijkman Institute Light Microscope Parasite Non Parasite Classification Microscopic Image Key Step Medical Practitioner Serious Global Health Burden Multiple Threshold Accurate Laboratory Diagnostic Tool Status Identification Segmented Image Global Thresholding Abstract Development Microscopy Diagnosis Novel Image Processing Algorithm Thin Smear Plasmodium Falciparum Specie Image Variability
Content Type	Text

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