Computer Vision for Malaria Parasite Classification in Erythrocytes

In this is paper, we introduce a new approach to represent a mathematical modeling technique by means of linear programming as an efficient tool to solve problems related to medical imaging problems especially Malaria Diagnosis through Microscopy Imaging problems .Two applications are approached: formulation of a linear programming based on the given data and solving the given problem using graphical method approach for detecting parasite. Also we applied some image processing techniques namely image segmentation, morphological operations. In the first application, just we have to develop mathematical model from the collected information and in second approach we have to solve problem by Graphical approach. We mark region infected with malaria from the original image leads to identifying parasite and also we classified different species of malaria by using graphical approach. By observation of graph we can predict whether the blood is infected by parasite or not. We can also classify the number of species of parasite infected the erythrocytes and parasite identification by labeling the infected area. INDEX TERMS Image processing, Microscopic imaging, Malaria Blood images, Red Blood Cells, Segmentation. BACKGROUND Malaria is a serious infectious disease caused by a peripheral blood parasite of the genus Plasmodium. According to the World Health Organization (WHO), it causes more than 1 million deaths arising from approximately 300– 500 million infections every year [1]. Although there are newer techniques, manual microscopy for the examination of blood smears (invented in the late 19th century), is currently "the gold standard" for malaria diagnosis. Diagnosis using a microscope requires special training and considerable expertise. It has been shown in several field studies that manual microscopy is not a reliable screening method when performed by non experts due to lack of training especially in the rural areas where malaria is endemic. An automated system aims at performing this task without human intervention and to provide an objective, reliable, and efficient tool to do so. But this work had done by other authors [2]. Microscopy diagnosis is performed by manual visual examination of blood smears[10]. The whole process requires an ability to differentiate between non parasitic stained components (e.g. red blood cells, white blood cells, platelets etc.,) and the malarial parasites using visual information. If the blood sample is diagnosed as positive (i.e. parasites present) an additional capability of differentiating species and life-stages (i.e. identification) is required to specify the infection. On light microscopic examination of the blood film the Morphological stage of the parasites can be reported (Fig.1.1). In order to perform diagnosis on peripheral blood samples, the system must be capable of differentiating between malarial parasites and healthy blood components. The majority of existing malaria-related image analysis studies doesn’t address these requirements. A brief introduction about the malaria parasite, its species and life-cycle stages is provided in the next section. J.Somasekar / International Journal on Computer Science and Engineering (IJCSE) ISSN : 0975-3397 Vol. 3 No. 6 June 2011 2251