Computer-aided Detection of Malaria Parasites

Malaria is a leading cause of death and disease in many developing countries, where young children and pregnant women are the most affected groups. In 2016, there were an estimated 216 million cases of malaria in 91 countries, which caused approximately 445 000 deaths. Around 90% of those cases occurred in Africa, where the lack of access to malaria diagnosis is largely due to shortage of expertise and equipment. Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. These drawbacks are closely related with the increasing interest in the development of computer-aided diagnosis systems, particularly distributed solutions that provide access to complex diagnosis in rural areas. Promising advances have been reported in the area of computer-aided detection of malaria parasites during the past few years. However, the majority of the proposed approaches in the literature are based on two main requirements unsuitable for most malaria-endemic areas: images acquired under well-controlled conditions and the need of proper microscopic equipment. Both criteria are difficult to accomplish in those areas, where this type of equipment and the know-how to maneuver it are scarce or nonexistent. The search for suitable alternatives for these scenarios has been the main driving force behind this thesis. Consequently, the objectives of this thesis revolved around making contributions in the field of computer-aided detection of malaria parasites using mobile devices. These contributions were envisioned to be the main building blocks for the development of a mobile-based framework supporting the pre-diagnosis of malaria in medically-underserved areas, being simultaneously low cost and easy to use, even for non-experts in microscopy. To achieve that goal, the development of both hardware and software components were explored. We started by developing a fully automated 3D-printed smartphone microscope with a motorized stage, termed μSmartScope. The developed prototype allows autonomous acquisition of a predefined number of images at 1000x magnification, by using a motorized automated stage fully powered and controlled by a smartphone, without the need of manual focus. In order to validate the prototype as a reliable alternative to conventional microscopy, we evaluated the μSmartScope performance in terms of: resolution; field of view; illumination; motorized stage performance (more specifically the mechanical movement precision/resolution and power consumption); and the proposed automated focus procedure. Furthermore, we also explored the development of computational approaches for the automated detection of malaria parasites in microscopic blood smear images acquired with the μSmartScope. Particularly, we proposed two different image processing methodologies using supervised classification: i) an approach to detect the presence and count the number of malaria parasites in thick blood smears; and ii) an approach to determine the species and life cycle stage of malaria parasites in thin blood smears. The promising results achieved by both methodologies attested the potential of using these approaches as a valid alternative to conventional microscopy examination.