Malaria is the leading cause of morbidity and mortality in tropical and subtropical countries. Conventional microscopy used in diagnosis of the disease has occasionally proved inefficient since it is time consuming and results are difficult to reproduce. Alternative diagnosis techniques which yield superior results are quite expensive and hence inaccessible to developing countries where the disease is endemic. In this work, Algorithms for automating diagnosis of malaria using thin blood smear RGB microscopy images are have been developed and implemented using Matlab software. Morphological, colour and texture features of Plasmodium parasites and erythrocytes are used to train Artificial Neural Network classifiers to identify sample images as infected or non-infected. Infected sample images are also classified into their respective life stages and species of Plasmodia and finally parasitemia is determined. This book is ideal for students and researchers pursuing research in image processing and computer vision.