Detection of sickle cell anaemia and thalassaemia causing abnormalities in thin smear of human blood sample using image processing

About 3.2 million people suffer from sickle-cell disease. Aim of this paper is to detect sickle cell anaemia and thalassaemia. The proposed method involves acquisition of the thin blood smear microscopic images, pre-processing by applying median filter, segmentation of overlapping erythrocytes using marker-controlled watershed segmentation, applying morphological operations to enhance the image, extraction of features such as metric value, aspect ratio, radial signature and its variance, and finally training the K-nearest neighbor classifier to test the images. The algorithm processes the infected cells increasing the speed, effectiveness and efficiency of training and testing. The K-Nearest Neighbour classifier is trained with 100 images to detect three different types of distorted erythrocytes namely sickle cells, dacrocytes and elliptocytes responsible for sickle cell anaemia and thalassemia with an accuracy of 80.6% and sensitivity of 87.6%.