Characterization of biofilm structure and properties via processing of 2D optical coherence tomography images in BISCAP

Abstract

Motivation: Processing of Optical Coherence Tomography (OCT) biofilm images is currently restricted to a set of custom-made MATLAB scripts. None of the tools currently available for biofilm image processing (including those developed for Confocal Laser Scanning Microscopy-CLSM) enable a fully automatic processing of 2D OCT images.

Results: A novel software tool entitled Biofilm Imaging and Structure Classification Automatic Processor (BISCAP) is presented. It was developed specifically for the automatic processing of 2D OCT biofilm images. The proposed approach makes use of some of the key principles used in CLSM image processing, and introduces a novel thresholding algorithm and substratum detection strategy. Two complementary pixel continuity checks are executed, enabling very detailed pixel characterizations. BISCAP delivers common structural biofilm parameters and a set of processed images for biofilm analysis. A novel biofilm 'compaction parameter' is suggested. The proposed strategy was tested on a set of 300 images with highly satisfactory results obtained. BISCAP is a Python-based standalone application, not requiring any programming knowledge or property licenses, and where all operations are managed via an intuitive Graphical User Interface. The automatic nature of this image processing strategy decreases biasing problems associated to human-perception and allows a reliable comparison of outputs.

Availability and implementation: BISCAP and a collection of biofilm images obtained from OCT scans can be found at: https://github.com/diogonarciso/BISCAP.

Supplementary information: Supplementary data are available at Bioinformatics online.