Search PubMed⌕ Search

Biomedical subjects

H Beyenal

Publications and source records attributed to H Beyenal.

4 recordsLinked to original sources
PubMed2001

Evaluation of biofilm image thresholding methods.

To evaluate biomass distribution in heterogeneous biofilms from their microscope images, it is often necessary to perform image thresholding by converting the gray-scale images to binary images consisting of a foreground of biomass material and a background of interstitial space. The selection of the gray-scale intensity used for thresholding is arbitrary but under the control of the operator, which may produce unacceptable levels of variability among operators. The quality of numerical information extracted from the images is diminished by such variability, and it is desirable to find a method that improves the reproducibility of thresholding operations. Automatic methods of thresholding provide this reproducibility, but often at the expense of accuracy, as they consistently set thresholds that differ significantly from what human operators would choose. The performance of five automatic image thresholding algorithms was tested in this study: (1) local entropy; (2) joint entropy; (3) relative entropy; (4) Renyi's entropy; and (5) iterative selection. Only the iterative selection method was satisfactory in that it was consistently setting the threshold level near that set manually. The extraction of feature information from biofilm images benefits from automatic thresholding and can be extended to other fields, such as medical imaging.

Algorithms↗
PubMed2001

Growing reproducible biofilms with respect to structure and viable cell counts.

We have developed a new method of growing 4-day-old biofilms that are reproducible, with respect to viable cell number and biofilm structure. To demonstrate the utility of the method, we grew biofilms composed of Pseudomonas aeruginosa (ATCC#700829), P. fluorescens (ATCC#700830) and Klebsiella pneumoniae (ATCC#700831), 18 times in flat-plate reactors under well-defined conditions of: flow rate, nutrient concentration, temperature, inoculum and growth rate. The resulting 4-day-old biofilms were approximately 200-300 microm thick and exhibited a high degree of reproducibility. The number of viable cells that accumulated per unit surface area and the biofilm areal porosity were reproduced within 10% error. We have also quantified other parameters characterizing biofilm structure using biofilm-imaging techniques: fractal dimension, textural entropy and diffusion distance as auxiliary parameters characterizing the reproducibility of biofilm accumulation. As a result of analysis, we have introduced a new parameter to better quantify and characterize the number of viable cells in biofilms, "specific number of viable cells" (SNVC). This parameter is the viable cell number normalized with respect to the surface area covered by the biofilm and with respect to the biomass of the biofilm. This new descriptor represents the dynamics of biofilm accumulation better than the traditionally used colony-forming unit (CFU) per surface area covered by the biofilm because it accounts not only for the surface coverage but also for the biofilm thickness.

Bacteriological Techniques↗
PubMed2000

Quantifying biofilm structure using image analysis.

We have developed and implemented methods of extracting morphological features from images of biofilms in order to quantify the characteristics of the inherent heterogeneity. This is a first step towards quantifying the relationship between biofilm heterogeneity and the underlying processes, such as mass-transport dynamics, substrate concentrations, and species variations. We have examined two categories of features, areal, which quantify the relative magnitude of the heterogeneity and textural, which quantify the microscale structure of the heterogeneous elements. The feature set is not exhaustive and has been restricted to two-dimensional images to this point. Included in this paper are the methods used to extract the structural information and the algorithms used to quantify the data. The features discussed are porosity, fractal dimension, diffusional length, angular second moment, inverse difference moment and textural entropy. We have found that some features are better predictors of biofilm behavior than others and we discuss possible future directions for research in this area.

Biofilms↗