Digital image analysis for automatic enumeration of malaria parasites using morphological operations

Abstract

Every year, malaria kills between 660,000 and 1.2 million people, many of whom are children in Africa. The World Health Organization (WHO) encourages the development of rapid and economical diagnostic tests that allow for the identification of proper treatment methods. In this paper a novel method to automatically enumerate malaria parasites is proposed and evaluated, using a database consisting of 475 images with varying densities of malaria parasites. This method will analyze data by utilizing standard operations of image processing such as histogram equalization, thresholding, morphological operations and connected components analysis for parasite density estimation. The application of the proposed method yields an average accuracy rate of 96.46% with a low processing time of two seconds per image on a custom computing platform.