Hybrid segmentation of fluorescent Leschmania-infected images using a watersched and combined region merging based method.

Automatic parasite segmentation in fluorescent images is of high importance as it serves as an easier and faster tool for detecting and counting parasites in each focus. In this paper we present a hybrid segmentation for the Promastigote form of Leishmania parasites in Indirect Immunofluorescence (IIF) images, combining edge and region-based techniques through the morphological algorithm of watershed. The proposed approach deals first with a pre-processing step to correct illumination non-uniformities in the fluorescence Leishmania-infected images before performing the initial segmentation by means of the watershed algorithm. A merging step using joint region homogeneity and edge integrity criteria is then applied to improve the segmentation results. Segmentation tests of 1438 parasites from 40 collected IIF images illustrate the efficiency of our approach.

Adaptive automatic segmentation of Leishmaniasis parasite in Indirect Immunofluorescence images.

This paper describes the first steps for the automation of the serum titration process. In fact, this process requires an Indirect Immunofluorescence (IIF) diagnosis automation. We deal with the initial phase that represents the fluorescence images segmentation. Our approach consists of three principle stages: (1) a color based segmentation which aims at extracting the fluorescent foreground based on k-means clustering, (2) the segmentation of the fluorescent clustered image, and (3) a region-based feature segmentation, intended to remove the fluorescent noisy regions and to locate fluorescent parasites. We evaluated the proposed method on 40 IIF images. Experimental results show that such a method provides reliable and robust automatic segmentation of fluorescent Promastigote parasite.