title: Multi-resolution border segmentation for measuring spatial heterogeneity of mixed population biofilm bacteria.
abstract: Multi-resolution image clustering and segmentation interactive system has been developed to analyze the interaction between clusters of heterogeneous microbial populations residing in biofilms. Biofilms are biological microorganisms attached to surfaces, which develop a complex heterogeneous three-dimensional structure. The hierarchical structural analysis concept underlying multi-resolution image segmentation is that the clusters will be more complex and noisy for higher-resolution while less complex and smoother for lower-resolution image. This hierarchical structure analysis can be used to simplify the image storage and retrieval in well-mixed populations. We are proposing an algorithm that combines Fuzzy C-Means, SOM and LVQ neural networks to segment and identify clusters. The outcome of the image segmentation is quantified by the number of cluster objects of each kind of microorganism within sections of the biofilm, and the centroid distances between the identified cluster objects. Experimental evaluations of the algorithm showed its effectiveness in enumerating cluster objects of bacteria in dual-species biofilms at the substratum and measuring the associated intercellular distances.
authors.affiliation: Georgia State University, University Plaza, Atlanta, GA 30303-3083, United States. sbelkasim@cs.gsu.edu
authors.forename: Saeid
authors.initials: S
authors.lastname: Belkasim
authors.forename: Gordana
authors.initials: G
authors.lastname: Derado
authors.forename: Rizi
authors.initials: R
authors.lastname: Aznita
authors.forename: Eric
authors.initials: E
authors.lastname: Gilbert
authors.forename: Heather
authors.initials: H
authors.lastname: O'Connell
mesh_terms: D000465:Algorithms; D001419:Bacteria; D018441:Biofilms; D016000:Cluster Analysis; D015169:Colony Count, Microbial / Q000379:methods; D005110:Extracellular Space; D007091:Image Processing, Computer-Assisted; D008954:Models, Biological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity