APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Mar. 2009, p. 1734–1739 Vol. 75, No. 60099-2240/09/$08.00�0 doi:10.1128/AEM.02000-08Copyright © 2009, American Society for Microbiology. All Rights Reserved.

Image Analysis Software Based on Color Segmentation forCharacterization of Viability and Physiological Activity

of Biofilms�

Luis E. Chavez de Paz*Department of Oral Biology, Faculty of Odontology, Malmo University, Malmo 20506, Sweden

Received 28 August 2008/Accepted 4 January 2009

The novel image analysis software package bioImage_L was tested to calculate biofilm structural parametersin oral biofilms stained with dual-channel fluorescent markers. By identifying color tonalities in situ, thesoftware independently processed the color subpopulations and characterized the viability and metabolicactivity of biofilms.

Automated and semiautomated digital image processingmethods extract quantitative data about the structure and topo-graphical distribution of a biofilm in two and three dimensions(1, 3, 4, 8, 12–14). To date, only a few software packages haveemphasized analyzing color image data to determine thedistribution and structure of microbial subpopulations (3,8). Typically, most color image analysis methods use theoriginal confocal image data file to perform a monochro-matic segmentation of the image.

This report’s main purpose is to present bioImage_L, whichenables in situ color segmentation without prior transforma-tion of micrographs into monochrome channels. The applica-bility of the software was tested to determine the baselinephysiology of dental plaque grown in a mini-flow cell systemand the changes to the physiological parameters when dentalplaque was subjected to different stress conditions.

Biofilm preparation and image acquisition. Dental plaquesamples were taken from the buccal and lingual surfaces oflower first and second molars with a dental probe. Sampleswere taken from the gingival margin and supragingival surfacesand suspended in vials containing 0.2 ml peptone-yeast extract-glucose (5) supplemented with a 10 mM potassium phosphatebuffer. Biofilms were created in triplicate in the mini flow-chamber system �-Slide VI for live cell analysis (IntegratedBioDiagnostics, Munich, Germany) as in a previous study (2).Briefly, 100 �l of the plaque samples was inoculated into theflow chamber system and incubated in an atmosphere of 5%CO2 in air at 37°C for 24 h. The baseline physiology of thebiofilms was determined by staining them with four fluorescentstains: the BacLight Live/Dead stain (Molecular Probes, Eugene,OR) to measure cell integrity, carboxy-SNARF-1 (MolecularProbes) to measure the intracellular pH, 5-cyano-2,3-ditolyl-tet-razolium chloride (CTC) to measure the dehydrogenase activity,and fluorescein diacetate (FDA) to measure the esterase activity.

The biofilms were examined with an Eclipse TE2000 in-verted confocal scanning laser microscope (CLSM) (Nikon

Corporation, Tokyo, Japan). The images were automaticallyacquired with the MultiPoint series macro as a supplement toNikon’s CLSM interface software EZ-C1 version 3.40, build691 (Nikon Corporation, Tokyo, Japan). CLSM images wereacquired with a 60� oil immersion objective with a numericalaperture of 1.4, and the confocal pinhole was set to a diameterof 30 �m. Images were acquired with a zoom factor of 1.0, apixel resolution of 0.42 �m/pixel, and a field resolution of 512by 512 pixels. Each stack had a substratum coverage field areaof 215 �m by 215 �m. In all cases, the z step for images in astack was 2 �m, and 10 stacks, composed of 10 two-dimen-sional (2-D) images, were acquired from each biofilm chamber.The image stacks were serially transformed from the CLSMformat Image Display Subsystem to the tiff format using amacro in the EZ-C1 software. The acquired images were pro-cessed through the general user interface of bioImage_L.

GUI. The general user interface (GUI) of bioImage_L wascreated with the Matlab guide tool (The MathWorks Inc.,MA). The GUI’s main purpose is to allow easy interaction withthe implemented image analysis tools, which primarily supportinput file preparation and output file displays, as well as fastdata preprocessing and processing, structural calculations ofbiofilm populations, and graphical displays of individual color-based subpopulations with graphic outputs of the results(see http://www.bioimageL.com/get_bioimage_L). The pro-gram was created with Matlab 7.4 (R2007a) using the WindowsXP SP2 operating system on a computer with a 1.24-GHzcentral processing unit and 1 GB of random access memory.

2-D cell counting and in situ color segmentation. The 2-Dcell counting routine simultaneously segments red and greenclasses in CLSM color microphotographs and is the basic unitof analysis for other functions in bioimage_L. To test thisfunction, an image of a biofilm section stained with CTC (red)and Syto24 (green) was processed (Fig. 1). After inputting theimage scale (0.42 �m/pixel), the user was asked to select thenoise-reducing factor (NRF). The NRF is a standard deviationsigma included in a scalar averaging filter as a square matrix(default: 3 by 3 pixels), which complements a Gaussian low-pass filter. After the NRF is selected, the threshold is deter-mined by an automatic method proposed by Otsu (9), in whichan intermediate point in the pixel intensity histogram of the

* Mailing address: Department of Oral Biology, Faculty of Odon-tology, Malmo University, Malmo SE-20506, Sweden. Phone: 46 406658659. Fax: 46 40 929359. E-mail: luis.chavez.de.paz@od.mah.se.

� Published ahead of print on 9 January 2009.

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image that corresponds to the threshold value is identified.From the threshold segmentation, the percentage of the areacovered by cells can be calculated (Fig. 1a).

Subsequently, bioImage_L applies an in situ color segmen-tation routine that automatically segments the color image intoindividual pseudochannels, and the areas and percentages ofeach identified color subpopulation are calculated and pre-sented. The principle of the color segmentation routine (avail-able for download at http://www.mathworks.com/matlabcentral/fileexchange) relies on the color addition theory (6) andclassifies each pixel of the image into a predefined color class,resulting in the generation of pseudochannels. The color to besegmented is identified by red, green, and blue codes (Rs, Gs,and Bs), which are subtracted from each pixel in the image.The absolute differences (R1diff, G1diff, and B1diff) betweenall three components in each pixel are represented as follows:R1diff � R1 � Rs for the red component, G1diff � G1 � Gsfor the green component, and B1diff � B1 � Bs for the bluecomponent. Subsequently, the three obtained differences arechecked to determine whether they are within the standardtolerance value (i.e., less than the tolerance default value of0.001).

If all three differences are within this tolerance value, it isdetermined that the selected color is present in that pixel. TheMatlab code returns a pseudochannel of the image with thepixels that have passed the color segmentation test. The colorsegmentation routine in bioImage_L has been implementedwith a simultaneous segmentation of red and green classes,since these colors are used by most common fluorescent labels

(segmentation of blue color is also implemented; see http://www.bioimageL.com/get_bioimage_L). As shown in Fig. 1bto e, the image is segmented into four green pseudochannelsand then merged as the total green subpopulation area in Fig.1f. In this example, the inactive (green) subpopulation arearepresents 76% of the original population. Similarly, four dif-ferent tonalities of red are segmented in Fig. 1g to j, resultingin a total CTC-active (red) area (Fig. 1k) that represents 24%of the total population in the sample biofilm. The correspond-ing pseudochannel codes and ratios are presented in Table 1.

Surface and volume distribution. One of the main advan-tages of imaging biofilms with a confocal microscope is that aseries of z axis scans is produced, which enables the reconstruc-tion of 3-D profiles. The surface and volume distribution func-

FIG. 1. Color segmentation method implemented in bioImage_L. (a) 2-D section of a 24-h biofilm stained with the CTC metabolic marker (redfluorescence) and the Syto24 counterstain (green fluorescence). This method segments the original color image into four green pseudochannels(b to e) and four red pseudochannels (g to j) (see Table 1 for further specification of the pseudochannel codes and proportional ratios). The totalgreen mask that results from the merging of the four green pseudochannels is shown in panel f. This represents 74% of the total population, withno detectable metabolism by CTC. The red mask is shown in panel k. This mask represents CTC-active cells, accounting for 24% of the totalpopulation. Bar, 50 �m.

TABLE 1. Red, green, and blue color coding and correspondingpercentages of identified pseudochannels in green and red

segments of Fig. 1a

Segment Fig. 1 panel showingpseudochannel

Color coding(red, green, blue) % in segment

Green b 118, 238, 0 13c 50, 205, 50 39d 0, 205, 0 34e 0, 139, 69 87

Red g 238, 44, 44 47h 255, 0, 0 38i 192, 0, 0 59j 139, 0, 0 67

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tion in bioImage_L was tested to compare the biofilm structureand distribution of independent subpopulations of cells grownon smooth uncoated polystyrene surfaces with those of cellsubpopulations grown on surfaces coated with salivary mucins(gelMUC5) (11). Biofilms were created on surfaces precondi-tioned with gelMUC5, incubated in an atmosphere of 5% CO2

in air at 37°C for 24 h, and stained with CTC. Biofilms werecounterstained with Syto24, which stained all cells fluorescentgreen.

The first step of the routine uses a parsing algorithm thatranks images by the stack name and identifies the last twonumbers in each image name, which correspond to its z posi-tion. After this parsing, the user is asked to input the scale(�m/pixel), NRF, and distance between the z layers. Then theuser is asked to select one stack, and the program runs the 2-Dcell counting routine for each image in the stack, while asubpanel shows the analyzed images. After completion of 2-Dcell counting, a subpanel shows the results for the total popu-lation and the subpopulations. The parameters in these resultsare biovolume, mean height, and substratum coverage. In ad-dition, green and red segments are reconstructed in three di-mensions (Fig. 2). This 3-D reconstruction is achieved with amodified version of the Matlab code, vol3d (available for down-load at http://www.mathworks. com/matlabcentral/fileexchange/).As shown in Fig. 2a to c, on the uncoated surface, dentalplaque bacteria covered 88% of the substratum surface. How-ever, 91% of the population showed no metabolic activity(green biovolume) (Fig. 2a). In addition, it appeared that the

few metabolically active cells (red subpopulation) were allo-cated in the upper layers of the biofilm (mean height of 17.3�m), probably where nutrients were more easily accessible(Fig. 2b). In contrast, the salivary mucin-coated surfaceshowed an uneven distribution of cells on the substratum, withsubstratum coverage of 52% (Fig. 2d to f). However, the pres-ence of mucins on the surface apparently activated the cells’metabolism, with 42% of the population stained fluorescentred by CTC (Fig. 2e). Similar vertical distributions between thered and the green metabolically inactive subpopulations werealso seen, with mean heights of 11.6 �m and 11.2 �m, respec-tively.

Analysis of biofilm populations. The function “viability andmetabolic activity of biofilms” in bioImage_L is designed toanalyze multiple stacks representing one biofilm in a singlerun. In Fig. 3, after the analysis is completed, the resultingvalues of biovolume, mean height, and substratum coverageare presented (Fig. 3e), in addition to a graph showing biomassvalues corresponding to different z levels and plots of the totalpopulation and the green and red subpopulations (Fig. 3f).

To determine experimental reproducibility, results obtainedfrom different biofilms were statistically compared with thefunction “viability and metabolic activity of biofilms (batch),”which analyzes different biofilms and presents the overallresults. A two-way analysis of variance (ANOVA) is auto-matically performed to give the significant differences in thevariabilities of the green and red subpopulations.

For three 24-h biofilm populations, the results on the viabil-

FIG. 2. 3-D reconstructions of dental plaque biofilms growing in mini-flow cell systems on an uncoated smooth polystyrene surface (a to c) anda saliva mucin-coated surface (d to f). The fluorescent stain used is CTC, which indicates metabolically active cells (red cells) and metabolicallyinactive cells (green). In panels a and d, the metabolically inactive green subpopulations are shown, while panels b and e show the active redsubpopulations. (c and f) 3-D reconstructions of the entire biofilm population. Axis units are �m.

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ity of dental plaque bacteria indicated that the biovolume ofthe subpopulation of microbes with undamaged cell mem-branes accounted for 96% � 2% of the total biofilm biovolume(Fig. 4a). No significant variation was detected by two-wayANOVA (P � 0.0001). When three of these populations wereexposed to 5% chlorhexidine gluconate for 30 min, the biovol-ume of the green population was reduced to 77% � 1%. Thecells that were in the upper levels, closer to the surface, weremore affected by the chlorhexidine exposure, although the

proportion of viable cells in the deeper biofilm layers was stillhigh (Fig. 4b).

In this report, carboxy-SNARF-1, a cell-permeable fluores-cent red dye that emits light in the presence of free ionsreleased due to extreme intracellular pH changes (7, 10), wasused in dental plaque biofilms. A working solution of carboxy-SNARF-1 was prepared by mixing 1 �l of 25 mM carboxy-SNARF-1 (Molecular Probes, Eugene, OR) with 999 �l ofphosphate-buffered saline. This mixture (40 �l) was added to

FIG. 3. GUI of the “viability and metabolic activity of biofilms” function in bioImage_L. (a) Command button to open a file path where folderscorresponding to biofilm(s) are allocated; (b) setting subpanel; (c) subpanel with the list of biofilms found and command buttons to select eitherone biofilm or all for analysis; (d) image display subpanel; (e and f) result subpanel (e) with a compiling graph on biomass and z level (f).

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FIG. 4. Baseline characteristics of dental plaque grown in vitro for 24 h in terms of (a) viability as measured with the BacLight Live/Dead stain, (c)intracellular pH as measured with carboxy-SNARF-1, (e) dehydrogenase activity as measured with CTC, and (g) esterase activity as measured with FDA. Theeffect of a 30-min exposure to 5% chlorhexidine gluconate on viability is shown in panel b, the effect of exposure to pH 3 for 30 min is seen in panel d, and theeffects of nutrient deprivation on dehydrogenase and esterase activities are seen in panels f and h, respectively. Error bars denote standard errors of triplicateexperiments. PBS, phosphate-buffered saline; EB, ethidium bromide.

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each tested biofilm chamber and counterstained with 1 �l of 1mM Syto24 (green fluorescence). The data obtained fromthree different biofilms showed that the biovolume of the sub-population with acidic intracellular pH (fluorescent red) rep-resented 7% � 3% of the total population (Fig. 4c). However,after exposure to extreme acid stress (pH 3) for 30 min, theproportion of the total biovolume with low intracellular pHincreased only to 35% � 4% (Fig. 4d).

In this study, the effect of 16 h of nutrient deprivation ondental plaque bacteria was also studied. The effect of nutrientdeprivation was measured by determining the levels of thedehydrogenase activity with CTC (Fig. 4e and f) and the es-terase activity with FDA (Fig. 4g and h). CTC was inoculatedat a concentration of 5 mM, and biofilms were counterstainedwith green Syto24 as described above. The FDA-ethidium bro-mide mixture was prepared and inoculated into the biofilmchambers as previously described (2). The biovolume of thedehydrogenase-active subpopulation (red) of 24-h dentalplaque bacteria was 52% � 4% of the total biovolume (Fig.4e); however, this value was reduced to 17% � 4% (ANOVAvariability not significant, P � 0.001) when the biofilms weredeprived of nutrients. Less dramatic was the reduction of theesterase activity, which showed a basal value of 87% � 6%(Fig. 4g) and was reduced to 71% � 5% of the total biofilmbiovolume (Fig. 4h).

Conclusions. By means of the novel in situ color segmenta-tion approach included in bioImage_L, the baseline physiologyof 24-h dental plaque was effectively monitored, as well as thephysiological changes occurring when dental plaque was sub-jected to different stress conditions. Application of this soft-ware as an alternative to monochrome image analysis process-ing could be of benefit in biofilm research since furtherimplementation of the software includes simultaneous segmen-tation of multiple color classes, e.g., when using multiple fluo-

rescence in situ hybridization probes within a biofilm popula-tion.

I thank Claes Wickstrom for providing the gelMUC5 for the surfaceand volume distribution experiment.

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