Advanced Image Analysis Based System for Automatic Detection and Classification of Malarial Parasite in Blood Images

This paper investigates the possibility of rapid and accurate automated diagnosis of red blood cell disorders and describes a method to detect and classify malarial parasites in blood sample images acquired from light microscopes. As malaria is an infectious disease which is mainly diagnosed by visual microscopical evaluation of Giemsa stained blood smears. As it poses a serious global health problem, automation of the evaluation process is of high importance. The image classification system is designed to positively identify malaria parasite in thin blood smears. Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cell) and possible parasites present on microscopic slides. Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. Classifier using back propagation feed forward neural network distinguishes between parasite infected and non-infected blood images.