Influence of Light Attenuation on Biofilm Parameters Evaluated from CLSM Image Data
Bacterial biofilms are microbial depositions growing on immersed surfaces and wetted interfaces in aqueous environments. They form when the bacterial cells attach to the surface and start producing an extracellular polymeric matrix. Bacterial biofilms play a positive role in waste water treatment, while they are a hindrance to treatment of bacterial infections in humans. Confocal Laser Scanning Microscopy (CLSM) is the method of choice widely used for studying structure formation of live biofilms in laboratory flow-cell reactors. CLSM data provide information about spatial organization in the biofilm, crucial to quantifying biofilm processes. Quantitative analysis of the CLSM data requires an accurate segmentation procedure that distinguishes biomass from the surrounding fluid. A survey of biofilm segmentation techniques in use reveals that the preferred method is (global) thresholding, which is simple but not optimal in the presence of noise and intensity attenuation (IA) artifacts inherently present in raw CLSM images. To remedy the effect of those artifacts, we propose a two-step preprocessing algorithm, which first corrects depth intensity attenuation using exact histogram specification (ERS) technique developed recently followed by a nonlinear diffusion filter which suppresses noise and homogenizes locally overenhanced image regions, thereby increasing robustness of the subsequent biofilm segmentation operation. In this study, we developed an automatic validation technique to estimate the effects of IA on various biofilm parameters and use it to demonstrate that IA is a significantly more detrimental source of error in comparison to noise and failure to take it into account may produce errors in biomass estimates of 50% or greater.