Two-stage feature extraction to identify Plasmodium ovale from thin blood smear microphotograph

Malaria is one of the tropical diseases, which is found with high prevalence in East side of Indonesia. To assist the diagnosis of this disease, we have developed a Computer Aided Diagnosis system that analyze the information obtained from thin blood smears microphotograph. Five species of the parasites are found in Indonesia, and each of them has different morphology. Therefore, the morphological characteristics of the parasites should be considered in the feature extraction development to obtain an accurate classifications system. The focus of this study is on the feature extraction development for Plasmodium ovale detection. A novel strategy is proposed by divide the feature extraction into two stages. The first stage focused on the most distinctive features, while the second focused on detail characteristics of the parasite. The proposed algorithm was evaluated using 177 microphotographs, obtained from Malaria observations carried out in various places of Indonesia by Eijkman Institute for Molecular Biology Indonesia, and showed a sensitivity of 0.75 and specificity 1.0.