Publication

Abstract : Purpose Identification of human intestinal parasites from microscopy images of fecal sample is an important and time consuming process in the diagnosis of intestinal parasitosis. Automatic image processing can be applied to segment and identify the parasite but the presence of fecal impurities makes this process a challenging one. This paper presents a framework for segmentation of bright field microscopy images of fecal sample that contain both parasites and impurities. Methods The proposed framework uses thresholding, morphological opening and Active Contour Model (ACM). Contour is initialized using thresholding and morphological opening and the contour is evolved using Localized Mean-Separation based Active Contour Model (LMS-ACM). Results This framework is simple, fast and yields good results even when the parasites are overlapped with impurities. The accuracy of the method is tested by comparing the results with manually segmented images. Conclusions As accurate segmentation of the objects is the first and important step in identification process, this work is a promising approach towards the automatic diagnosis of human intestinal parasitosis.