Thin Blood Smear Images Research Articles

Determination of <i>Plasmodium</i> Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network

Malaria is a leading cause of deaths globally. Rapid and accurate diagnosis of the disease is key to its effective treatment and management. Identification of plasmodium parasites life stages and species forms part of the diagnosis. In this study, a technique for identifying the parasites life stages and species using microscopic images of thin blood smears stained with Giemsa was developed. The technique entailed designing and training Artificial Neural Network (ANN) classifiers to perform the classification of infected erythrocytes into their respective stages and species. The outputs of the system were compared to the results of expert microscopists. A total of 205 infected erythrocytes images were used to train and test the performance of the system. The system recorded 99.9% in recognizing stages and 96.2% in recognizing plasmodium species.

Detection of plasmodium parasites from images of thin blood smears

Malaria is the leading cause of morbidity and mortality in tropical and subtropical countries. Conventional microscopy is the Gold standard in the diagnosis of the disease. However, it is prone to some shortcomings which include time consumption and difficultness in reproducing results. Alternative diagnosis techniques which yield superior results are quite expensive and hence inaccessible to developing countries where the disease is prevalent. Thus in this work, an accurate, speedy and affordable system of malaria detection using stained thin blood smear images was developed. The method uses Artificial Neural Network (ANN) to test for the presence of plasmodium parasites in thin blood smear images. Images of infected and non-infected erythrocytes were acquired, pre-processed, relevant features extracted from them and eventually diagnosis was made based on the features extracted from the images. Diagnosis entailed detection of plasmodium parasites. Classification accuracy of 95.0% in detection of infected erythrocyte was achieved with respect to results obtained by expert microscopists. The study revealed that artificial neural network (ANN) classifiers trained with colour features of infected stained thin blood smear images are suitable for detection. It was further shown that ANN classifiers can be trained to perform image segmentation.

Image Analysis Approach for Development of a Decision Support System for Detection of Malaria Parasites in Thin Blood Smear Images

This paper describes development of a decision support system for diagnosis of malaria using color image analysis. A hematologist has to study around 100 to 300 microscopic views of Giemsa-stained thin blood smear images to detect malaria parasites, evaluate the extent of infection and to identify the species of the parasite. The proposed algorithm picks up the suspicious regions and detects the parasites in images of all the views. The subimages representing all these parasites are put together to form a composite image which can be sent over a communication channel to obtain the opinion of a remote expert for accurate diagnosis and treatment. We demonstrate the use of the proposed technique for use as a decision support system by developing an android application which facilitates the communication with a remote expert for the final confirmation on the decision for treatment of malaria. Our algorithm detects around 96% of the parasites with a false positive rate of 20%. The Spearman correlation r was 0.88 with a confidence interval of 0.838 to 0.923, p<0.0001.