Segmentation based approach for detection of malaria parasites using moving k-means clustering

Recent progress based on microscopic imaging has given significant contribution in diagnosis of malaria infection based on blood images. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised colour image segmentation of malaria parasites using moving k-means (MKM) clustering algorithm. It has been applied on malaria images of P. vivax species. The proposed segmentation method provides a basic step for detection of the presence of malaria parasites in thin blood smears. With the aim of obtaining the fully segmented red blood cells infected with malaria parasites, the malaria images will firstly enhanced by using the partial contrast stretching technique. Then, the MKM clustering algorithm has been applied on the saturation and intensity components of HSI (hue, saturation, intensity) colour space for segmenting the infected cell from the background. After that, the segmented images have been processed using median filter and seeded region growing area extraction algorithms for smoothing the image and removing any unwanted regions from the image, respectively. Finally, the holes inside the infected cell are filled by applying region filling based on morphological reconstruction algorithm. The proposed segmentation method has been analyzed using 100 malaria images which consist of the trophozoite and gametocyte stages. Overall, the results indicate that MKM clustering that has been performed on saturation component image has produced the best segmentation performance with segmentation accuracy of 99.49% compared to the intensity component image with segmentation accuracy of 98.89%.