Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering

Abstract

Malaria is a serious global health problem that is responsible for nearly one million deaths each year. With the large number of cases diagnosed over the year, rapid detection and accurate diagnosis of malaria infection which facilitates prompt treatment are essential to control malaria. This paper presents a color image segmentation approach for detection of malaria parasites that has been applied on malaria images of P. vivax species. In order to obtain the segmented red blood cells infected with malaria parasites, the images are first enhanced by using partial contrast stretching. Then, an unsupervised segmentation technique namely k-means clustering has been used to segment the infected cell from the background. Different colour components of RGB, HSI and C-Y colour models have been analysed to identify colour component that can give significant segmentation performance. Finally, median filter and seeded region growing area extraction algorithms have been applied for smoothing the image and remove any unwanted regions from the image, respectively. The proposed segmentation method has been evaluated on 100 malaria images. Overall, segmentation using S component of C-Y colour model has proven to be the best in segmenting the malaria image with segmentation accuracy and F-score of 99.46% and 0.9370, respectively.