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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
Published on: February 15, 2022
Hybrid segmentation of fluorescent Leschmania-infected images using a watersched and combined region merging based
Abstract:
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.

