Malarial parasites detection in RBC using image processing

The gold standard for the diagnosis of malaria is microscopy in which the blood slide is examined under a microscope, but the reliability, accuracy and timely diagnosis of the results are highly based on the proficiency of the technician examining the slide. False Detection can occur in the case of poorly skilled technician. In this research work we have proposed a system for automating the manual work done by a technician in order to cut down the human error and increasing the accuracy of the malaria diagnosis. The System is tested for a dataset of 80 images of a thin blood smear. The infected cells are extracted using HSV segmentation. This approach will be beneficial for the rural areas, with a scarcity of experts.