The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Image Processing <s> This work describes a system for detecting and classifying malaria parasites in images of Giemsa stained blood slides in order to evaluate the parasitaemia of the blood. The first aim of our system is to detect the parasites by means of an automatic thresholding based on a morphological approach. Then we propose a morphological method to cell image segmentation based on grey scale granulometries and openings with disk-shaped elements, flat and hemispherical, that is more accurate than the classical watershed-based algorithm. The last step of the system is classifying the parasites by morphological skeleton. <s> BIB001 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Image Processing <s> There is always a need for the development of systems to provide assistance to physicians and to relieve the physician of drudgery or repetitive work. So, more systems for automatic processing of medical images are being developed. In case of malaria diagnosis, the usual method of estimating the parasitaemia is to take number of slides and examine them manually through a microscope and count parasitaemia which is not only a time consuming task and but also prone to human error. This paper discuss the development of a system to automatically digitise the malaria infected blood slides and process them to produce information such as parasitaemia, infected and total red blood cell count as required by the physicians and the field workers. The methodology for the blood slide image processing is developed using mathematical morphology. Because of its basis on analysis of shape and size and it is well suited for this application. This paper presents various steps involved and procedures evolved for the task of developing such a system. (6 pages) <s> BIB002 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Image Processing <s> A technique is proposed for estimating parasitaemia from blood smear images by extracting healthy and parasite infected red blood cells. The developed approach accounts for uncertain imaging conditions due to microscope settings as well as the quality of the blood smear preparation. The solution is based on a multi-stage estimation process with minimal prior knowledge starting from a model representation of red blood cells. Based on pattern matching with parameter optimisation and cross-validation against the expected biological characteristics, red blood cells are determined. In a final stage, the parasitaemia measure is carried out by partitioning the uninfected and infected cells using an unsupervised and in comparison a training-based technique. Finally, the obtained estimates were analysed with respect to manually acquired results from professionals. Red blood cells detection resulted in precision and recall rates of 80-88% and 92-98%, respectively. By using a training-based method, the precision and recall rates were improved to 92% and 95%, respectively. <s> BIB003 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Image Processing <s> Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to automate the diagnosis of malaria on thin blood smears is developed. The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images are acquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides. Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforward neural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P. vivax, P. ovale or P. malariae) of the infection. Malaria samples obtained from the Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand Medical School are used for training and testing of the system. Infected erythrocytes are positively identified with a sensitivity of 85% and a positive predictive value (PPV) of 81%, which makes the method highly sensitive at diagnosing a complete sample provided many views are analysed. Species were correctly determined for 11 out of 15 samples. <s> BIB004 </s> The Application of Biomedical Engineering Techniques to the Diagnosis and Management of Tropical Diseases: A Review <s> Image Processing <s> Malaria is a serious global health problem and rapid, precise determination of parasitemia is necessary for malaria research and in clinical settings. Manual counting by light microscopy is the most widely used technique for parasitemia determination but it is a time-consuming and laborious process. The aim of our study was to develop an automated image analysis-based system for the rapid and accurate determination of parasitemia. We have developed, for the first time, a software, MalariaCount, that automatically generates parasitemias from images of Giemsa-stained blood smears. The potential application and robustness of MalariaCount was tested in normal and drug-treated in vitro cultures of Plasmodium falciparum. The results showed a tight correlation between MalariaCount and manual count parasitemia values. These findings suggest that MalariaCount can potentially be used as a tool to provide rapid and accurate determination of parasitemia in research laboratories where frequent, large-scale, efficient determination of parasitemia is required. <s> BIB005
Many researchers have reported the auto-detection of malaria infection by utilizing image processing techniques on microscopy images  BIB001 BIB003 BIB002 BIB004 BIB005   . This work is based on images of cultured malarial parasites that were grown under a controlled environment rather than in patients' blood. Rao  utilized the stained images of the P. falciparum to analyse its life cycle. At a later study by Sio et al. BIB005 , new software that automatically counts the malarial parasite has been reported. The software focused on the counting of P. falciparum in images where the regular blood components and other types of noise are not present. The algorithm applied is able to differentiate between infected and uninfected red blood cells, and successfully count the parasites from peripheral blood specimens. Dimension and colour of components were used for identification. Halim et al BIB003 performed template matching techniques to detect red blood cells (RBC). Gray scale images were processed using different image process techniques for parasite detection. The second developed method utilizes the colour co-occurrence array that analyzes pixel colour index and the indicated colour of the surrounding pixels. The various techniques produced results with accuracy of 80%-88% and a sensitivity of 92%-98%.