Evaluation of biofilm image thresholding methods

To evaluate biomass distribution in heterogeneous biofilms from their micro scope images, it is often necessary to perform image thresholding by conver ting the gray-scale images to binary images consisting of a foreground of b iomass material and a background of interstitial space. The selection of th e gray-scale intensity used for thresholding is arbitrary but under the con trol of the operator, which may produce unacceptable levels of variability among operators. The quality of numerical information extracted from the im ages is diminished by such variability, and it is desirable to fmd a method that improves the reproducibility of thresholding operations. Automatic me thods of thresholding provide this reproducibility, but often at the expens e of accuracy, as they consistently set thresholds that differ significantl y 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) iter ative selection. Only the iterative selection method was satisfactory in th at it was consistently setting the threshold level near that set manually. The extraction of feature information from biofilm images benefits from aut omatic thresholding and can be extended to other fields, such as medical im aging. (C) 2001 Elsevier Science Ltd. All rights reserved.