Automatic status identification of microscopic images obtained from malaria thin blood smear
Development of an accurate laboratory diagnostic tool, as recommended by WHO, isrnthe key step to overcome the serious global health burden caused by malaria. Thisrnstudy aims to explore the possibility of computerized diagnosis of malaria and torndevelop a novel image processing algorithm to reliably detect the presence of malariarnparasite from Plasmodium falciparum species in thin smears of Giemsa stainedrnperipheral blood sample. The algorithm was designed as an expert system based onrnthe method used by medical practitioner performing microscopy diagnosis of malaria.rnDigital images were acquired using a digital camera connected to a light microscope.rnPrior to processing, the images were subjected to gray-scale conversion to decreaserncolor variability. Global thresholding was implemented to obtain erythrocyte andrnother blood cell components in each image. The segmented images were furtherrnprocessed to obtain informative features that were further used in classification stage.rnTwo-stage classification using selected features was built based on Bayesian DecisionrnTheory. Malaria samples, prepared and provided by Eijkman Institute of MolecularrnBiology Indonesia, were used to build and test the proposed algorithm.
|B01052||(wh)||Available but not for loan - Missing|
No other version available