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 princi...

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Bibliographic Details
Published in2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2014; pp. 4731 - 4734
Main Authors Ouertani, F., Amiri, H., Bettaib, J., Yazidi, R., Ben Salah, A.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2014
Subjects
Algorithms
Animals
Automation
Cluster Analysis
Dogs
Feature extraction
Fluorescence
Fluorescent Antibody Technique, Indirect - methods
Fuzzy Logic
Image color analysis
Image Processing, Computer-Assisted
Image segmentation
Immune system
Leishmaniasis - parasitology
Microscopy
Noise measurement
Parasites - cytology
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Summary: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.
ISSN:1094-687X
1557-170X
1558-4615
DOI:10.1109/EMBC.2014.6944681