Center for Biofilm Engineering
Abstract:
"Evaluation of Biofilm Image Thresholding Methods"
01-004 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 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 operation. 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 chose. 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.
Xinmin, Y., H. Beyenal, G. Harkin, Z. Lewandowski, "Evaluation of
Biofilm Image Thresholding Methods," Wat. Res., 35(5):1149-1158 (2001).
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