title: Adaptive automatic segmentation of Leishmaniasis parasite in Indirect Immunofluorescence images.
abstract: 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.
authors.forename: F
authors.initials: F
authors.lastname: Ouertani
authors.forename: H
authors.initials: H
authors.lastname: Amiri
authors.forename: J
authors.initials: J
authors.lastname: Bettaib
authors.forename: R
authors.initials: R
authors.lastname: Yazidi
authors.forename: A
authors.initials: A
authors.lastname: Ben Salah
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D016000:Cluster Analysis; D004285:Dogs; D019084:Fluorescent Antibody Technique, Indirect / Q000379:methods*; D017143:Fuzzy Logic; D007091:Image Processing, Computer-Assisted; D007896:Leishmaniasis / Q000469:parasitology*; D010271:Parasites / Q000166:cytology*