Unsupervised color image segmentation of red blood cell for thalassemia disease

Abstract

Current diagnosis of thalassemia disease based on peripheral blood images has given significant contribution to the field of hematology. Due to the requirement of prompt and accurate diagnosis of the disease, this study has proposed an unsupervised color image segmentation of thalassemia disease using moving k-means (MK) clustering algorithm. It has been applied on blood sample images of both types of thalassemia including normal (healthy) blood sample. The proposed segmentation method provides a basic step of red blood cell detection in thin blood smears. With the aim of obtaining the fully segmented abnormal and normal red blood cells, the blood images will firstly enhanced by using the global contrast technique. Then, the MK clustering algorithm has been applied on each of the HSI (hue, saturation, intensity) color components to segment the blood cells from the background. After that, the segmented images have been processed using median filter for smoothing the image. By comparing the segmentation accuracy of the segmented blood cells, the best one will be processed using seeded region growing area extraction algorithms based on saturation color component for removing any unwanted regions from the image and to obtain the segmented red blood cells. The proposed segmentation method has been analyzed using 60 blood images which consist of α-thalassemia, β-thalassemia, β-thalassemia trait and normal blood sample. Overall, the results indicate that the proposed method performed on intensity component image has produced better results based on segmentation performances with average values of 94.57% accuracy.