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Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(2), 513;
Received: 14 December 2017 / Revised: 29 January 2018 / Accepted: 5 February 2018 / Published: 8 February 2018
(This article belongs to the Special Issue Novel Sensors for Bioimaging)
PDF [8044 KB, uploaded 8 February 2018]


Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome the issues, numerous methods of automatic malaria diagnosis have been proposed so far. In particular, many researchers have used mathematical morphology as a powerful tool for computer aided malaria detection and classification. Mathematical morphology is not only a theory for the analysis of spatial structures, but also a very powerful technique widely used for image processing purposes and employed successfully in biomedical image analysis, especially in preprocessing and segmentation tasks. Microscopic image analysis and particularly malaria detection and classification can greatly benefit from the use of morphological operators. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection and identification in stained blood smears images. View Full-Text
Keywords: malaria; red blood cells segmentation; mathematical morphology; medical image analysis malaria; red blood cells segmentation; mathematical morphology; medical image analysis

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Loddo, A.; Di Ruberto, C.; Kocher, M. Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology. Sensors 2018, 18, 513.

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