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Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection. [ABSTRACT FROM AUTHOR]

Anaemia is one of the most common diseases in the world population. Primarily anaemia is identified based on haemoglobin level; and then microscopically examination of peripheral blood smear is required for characterizing and confirmation of anaemic stages. In conventional approach, experts visually characterize abnormality present in the erythrocytes under light microscope, and this evaluation process is subjective in nature and error prone. In this study, we have proposed a methodology using machine learning techniques for characterizing erythrocytes in anaemia associated with anaemia using microscopic images of peripheral blood smears. First, peripheral blood smear images are preprocessed based on grey world assumption technique and geometric mean filter for reducing unevenness of background illumination and noise reduction. Then erythrocyte cells are segmented using marker-controlled watershed segmentation technique. The erythrocytes in anaemia, such as, tear drop, echinocyte, acanthocyte, elliptocyte, sickle cells and normal erythrocytes cells have been characterized and classified based on their morphological changes. Optimal subset of features, ranked by information gain measure provides highest classification performance using logistic regression classifier in comparison with other standard classifiers. [ABSTRACT FROM AUTHOR]