Automatic Detection of Malaria Parasite from Blood Images

Malaria is a serious disease for which the immediate diagnosis is required in order to control it. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection being made about the disease. If the wrong detection is done then the disease can turn into more severe state. So the study about the computerized diagnosis is done in this paper, which will help in immediate detection of the disease to some extent, So that the proper treatment can be provided to the malaria patient. Also the image processing algorithm is used which will reliably detect the presence of malaria parasite from Plasmodium falciparum species in thin smears of Giemsa stained peripheral blood sample. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. This paper removes the human error while detecting the presence of malaria parasites in the blood sample by using image processing and automation. This is achieved by using Image Segmentation techniques to detect malaria parasites in images acquired from Giemsa stained peripheral blood samples. This is comparative study of two methods for detecting malaria parasites, first method is based on segmentation and second uses feature extraction using minimum distance classifiers. We built the malaria detection system in a robust manner so that it is unaffected by the exceptional conditions and achieved high percentages of sensitivity, specificity, positive prediction and negative prediction values.