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 is designed as an expert system based on the method used by medical practitioner performing microscopy diagnosis of malaria. Digital images are acquired using a digital camera connected to a light microscope. Prior to processing, the images are subjected to grayscale conversion to decrease image variability. Global are implemented to obtain erythrocyte and other blood cell components in each image. The segmented images are 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) are used as the preliminary basis for parasite/non parasite classification. Malaria samples obtained from Eijkman Institute of Molecular Biology are used to test the proposed algorithm.
Paper: “Automatic Status Identification of Microscopic Images Obtained from Malaria Thin Blood Smears”, Dian Anggraini, Anto Satriyo Nugroho, Christian Pratama, Ismail Ekoprayitno, Aulia Arif Iskandar, Reggio Nurtanio Hartono, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) at the Institut Teknologi Bandung, Bandung, Indonesia on July 17-19, 2011 (accepted)
This research has been supported by the Ministry of Research and Technology, Government of Indonesia under Grant Insentif Riset RT 2011 – 2311