Soil-transmitted helminths (STH) are one of the causes of health problems in children and adults. Based on a large number of helminthiases cases that have been diagnosed, a productive system is required for the identification and classification of STH in ensuring the health of the people is guaranteed. This paper presents a fast and efficient method to segment two types of STH; Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO) based on the analysis of various color models. Firstly, the ALO and TTO images are enhanced using modified global contrast stretching (MGCS) technique, followed by the extraction of color components from various color models. In this study, segmentation based on various color models such as RGB, HSV, L*a*b and NSTC have been used to identify, simplify and extract the particular color needed. Then, k-means clustering is used to segment the color component images into three clusters region which are target (helminth eggs), unwanted and background regions. Then, additional processing steps are applied on the segmented images to remove the unwanted region from the images and to restore the information of the images. The proposed techniques have been evaluated on 100 images of ALO and TTO. Results obtained show saturation component of HSV color model is the most suitable color component to be used with the k-means clustering technique on ALO and TTO images which achieve segmentation performance of 99.06% for accuracy, 99.31% for specificity and 95.06% for sensitivity.
