Malaria Detection Using Image Processing

Abstract

Malaria is one of a serious infectious disease in the world caused by a peripheral blood parasite of the genus Plasmodium. Traditional microscopy method, for malaria diagnosis is old method and has been occasionally proved inefficient since it is time consuming and results are difficult to reproduce. As it constitutes a serious global health problem, the evaluation process is of high importance. In this work, an accurate, easy, rapid and affordable model of malaria diagnosis using stained thin blood smear images was developed. The method makes use of the intensity features of Plasmodium parasites and also erythrocyte or red blood cell images. Images of infected and non-infected red blood cells were taken, pre-processed, and then relevant features were extracted from them and eventually diagnosis was made based on the features extracted from the images. A set of features based on intensity have been proposed, and the performance of these features on the red blood cell samples from the database have been evaluated using an artificial neural network (ANN) classifier. The results have shown that these features could be extremely successful if used for malaria parasite detection.