Yang X, Beyenal H, Harkin G, Lewandowski Z
Center for Biofilm Engineering, Montana State University, Room 366 EPS, P.O. Box 173980, Bozeman, MT 59717-3980, USA.
Water Res. 2001 Apr;35(5):1149-58. doi: 10.1016/s0043-1354(00)00361-4.
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.
为了从显微镜图像评估异质生物膜中的生物量分布，通常需要通过将灰度图像转换为由生物量物质前景和间隙空间背景组成的二值图像来执行图像阈值处理。用于阈值处理的灰度强度选择是任意的，但由操作员控制，这可能会在操作员之间产生不可接受的变异性水平。这种变异性会降低从图像中提取的数值信息的质量，因此需要找到一种提高阈值处理操作可重复性的方法。自动阈值处理方法提供了这种可重复性，但通常以准确性为代价，因为它们始终设置与人类操作员选择的阈值有显著差异的阈值。本研究测试了五种自动图像阈值处理算法的性能：（1）局部熵；（2）联合熵；（3）相对熵；（4）雷尼熵；（5）迭代选择。只有迭代选择方法令人满意，因为它始终将阈值水平设置在手动设置的阈值附近。从生物膜图像中提取特征信息受益于自动阈值处理，并且可以扩展到其他领域，如医学成像。
