biofilm monitoring on rotating discs by image analysis

ARTICLE

Biofilm Monitoring on Rotating Discs byImage Analysis

Marie-Noelle Pons,1 Kim Milferstedt,2 Eberhard Morgenroth2,3

1Laboratoire des Sciences du Genie Chimique, CNRS, Nancy-Universite, INPL, 1,

rue Grandville, BP 20451, F-54001 Nancy Cedex, France; telephone: 33-3-83-17-52-77;

fax: 33-3-83-17-53-26; e-mail: [email protected] of Civil and Environmental Engineering, University of Illinois at

Urbana-Champaign, 3219 Newmark Civil Engineering Laboratory, Urbana, Illinois3Department of Animal Sciences, University of Illinois at Urbana-Champaign,

Animal Sciences Laboratory, Urbana, Illinois

Received 24 September 2008; revision received 26 November 2008; accepted 2 December 2008

Published online 8 December 2008 in Wiley InterScience (www.interscience.wiley.co
m). DOI 10.1002/bit.22222
ABSTRACT: The macrostructure development of biofilmsgrown in a lab-scale rotating biological contactor was mon-itored by analyzing the average opacity and the texture ofgray-level images of the discs. The reactor was fed withmunicipal or synthetic wastewater. Experiments lasted onaverage 4–14 weeks. The images were obtained with a flat-bed scanner. The opacity and its standard deviation aredirectly extracted from the annular zone where the biofilmdevelops. This zone is defined by the outer edge of the discand the waterline. The spatial gray-level dependence matrix(SGLDM) approach was used for the texture assessment.As this method requires rectangular images, a geometricaltransformation had to be developed to transform the ringinto a workable area. This transformation now allowsquantitative image analysis on circular biofilms. As a laststep, Principal Components Analysis was applied to the setof textural descriptors to reduce the number of texturalparameters. Opacity and textural information allowed thenon-intrusive monitoring of the growth/regrowth of thebiofilms as well as biofilm loss, due to detachment, auto-digestion, or protozoan grazing. Textural description wasvery valuable by helping to discriminate biofilms of similaropacity characteristics but presenting different macrostruc-tures.

Biotechnol. Bioeng. 2009;103: 105–116.

� 2008 Wiley Periodicals, Inc.

KEYWORDS: biofilm; image analysis; macrostructure;opacity; rotating disc; visual texture

Kim Milferstedt’s present address is Department of Microbiology, University of Illinois

at Urbana-Champaign, 601 S. Goodwin Avenue, Urbana, IL 61801.

Correspondence to: M.-N. Pons

Contract grant sponsor: National Science Foundation

Contract grant number: BES-0134104; CTS-0120978

Additional Supporting Information may be found in the online version of this article.

� 2008 Wiley Periodicals, Inc.

Introduction

Biofilms are composed of different types of microorganisms(bacteria, fungi, algae, protozoa, etc.). Many biofilmapplications can be found in wastewater treatment systemsto treat municipal as well as industrial wastewater(Najafpour et al., 2005; Nilsson et al., 2006 among manyothers). A common configuration of a biofilm reactor inwastewater treatment are rotating biological contactors orrotating discs. In these systems, biomass is fixed on a circularsolid medium which rotates slowly around its axis(Patwardhan, 2003). The disc is periodically exposed toair and wastewater. Excess biomass is stripped off by shearforces exerted during the passage of the disc in the water.Rate and location of detachment have a significant influenceon the microbial ecology within the biofilm (Morgenrothand Wilderer, 2000). Following the flow of water, the biofilmgrows as a ring on the wetted area of the disc. The unusualgeometry of the ring shaped biofilm requires differentalgorithms when analyzing images from rotating discsbiofilms during automated image analysis. In biofilmreactors such as the rotating disc reactors monitoringthe heterogeneous distribution of the biofilm over thesurface area is difficult due to the size and shape of thesurface (Okabe et al., 1995).

Recently an image analysis procedure using a simple flat-bed scanner was proposed by Milferstedt et al. (2006) tomonitor biofilm development on rectangular transparentslides in a high-shear bioreactor over several weeks, withoutdestruction or deterioration of the biofilm. With a flat-bedscanner it is possible to rapidly acquire images of largerareas of biofilms than in conventional light or confocalmicroscopy. Images capturing large areas are particularlyuseful when changes in macrostructure with respect todevelopment time are of interest. Biomass weight and

Biotechnology and Bioengineering, Vol. 103, No. 1, May 1, 2009 105

Figure 1. Experimental set-up: Perspective view (a) and side-view (b).

biofilm thickness were correlated to the gray level of thegrabbed images. Furthermore the visual texture, assessed bythe spatial Gray-Level Dependence Matrix (SGLDM)method (Haralick et al., 1973), could be correlated tobiofilm detachment events and the development ofmacrostructure (Milferstedt et al., 2007, 2008). Generallyspeaking texture assessment methods are able to compareimages with respect of pixel location and gray level. Sobiofilms with comparable global opacities but with differentspatial distributions of patches of various opacities can bediscriminated. Such features are already available in softwarededicated to biofilm study such as ISA3D (Beyenal et al.,2004).

The purpose of this study was to extend the scannermethod proposed by Milferstedt et al. (2006) by ageometrical transformation step to monitor biofilm growthby automated image analysis in rotating disc reactors. Manyimage analysis tools as for example SGLDM allow us toassess the spatial relationship between neighboring pixels.However, neighboring pixels in a ring shaped biofilm do notfollow the angular path dictated by the disc rotation but aretangential or perpendicular to the tangent.

Our transformation converts the ring-shaped geometry ofthe biofilm images to a rectangular grid. Consequently,image analysis tools that require rectangular images aresubsequently available also to images acquired on rotatingdiscs. A transformation is not necessary for the determina-tion of scale-independent gray level statistics (mean andstandard deviation) as mean gray level statistics are indepen-dent of the pixel neighborhood, that is, the gray levels of theeight neighbors of any pixel.

Table I. Experimental schedule (first day–last day) for each disc used in

the experiment.

Exp Disc d1 Disc d2 Disc d3 Disc d4 Disc d5 Disc d6 Disc d7

Run 4 0–55 0–34 20–55 34–55

Run 5 0–33 0–6

Run 6 0–42 0–42

Run 7 0–104 0–104 0–104 0–55 0–30 30–104 55–104

Run 9 0–67 0–23 0–67 23–67

Materials and Methods

Experimental Set-Up

Biofilm was grown on rotating (4 rpm) transparent plasticdiscs (diameter¼ 100 mm) in a 2-L rectangular tank(Fig. 1). With a spacing of 10 mm, five discs can be housedon the axis of the reactor. The tank was fed (flow rate¼100 mL/min) with synthetic wastewater or with primarysettled wastewater collected at a municipal wastewatertreatment plant. The average chemical oxygen demand(COD) of the municipal wastewater varied between 200 and300 mg/L. The concentrated synthetic substrate containedper liter 50.24 g of meat extract (Viandox1, Amora, Dijon,France), 2.71 g of sucrose, 2.16 g of NH4Cl and 0.29 mL ofphosphoric acid yielding a total COD of 16 g/L. This stocksolution was diluted with tap water to obtain the desiredfeed COD concentration (160 or 320 mg/L). At thebeginning of each run the reactor was inoculated withwastewater by operating in batch mode for 2 days. Theschedule of each run is summarized in Table I. Municipalwastewater was used as the feed in all experiments except inRun 4. For this run, municipal wastewater was fed until day34, at which time the feed was switched to a synthetic growth

106 Biotechnology and Bioengineering, Vol. 103, No. 1, May 1, 2009

medium (COD¼ 160 mg/L). On day 36, the feed COD wasincreased to 320 mg/L.

Analyses

The feed and effluent were regularly sampled. After filtration(paper filter of pore size �1.5 mm), the samples wereanalyzed for ammonia using the Nessler method with aHach DR2000 spectrophotometer after dilution with aHamilton auto-diluter. UV-visible spectra were collected onan Anthelie Light spectrophotometer (Secomam, Domont,France) using a quartz cuvette (path length¼ 1 cm).Deionized water was used as blank. Absorbance at254 nm was used as a measure for soluble organic matter(Howard et al., 2004) and was read off the spectrum. Thepeak at 215 nm was used as an indicator of the presence of

nitrate (Ferree and Shannon, 2001). Nitrate concentrationswere also occasionally measured by the cadmium reductionmethod on the Hach DR2000 spectrophotometer. Periodi-cally, a disc was sacrificed so the dry weight of the biomasscould be determined. For this measurement, the biomasswas detached from the disc by a gentle jet of deionized waterand a soft scrapper (to avoid scratches on the plastic surfaceof the disc) and filtered on a glass fiber filter (pore size¼1.2 mm). The filter was then dried in an oven at 1058C for24 h. Prior to destruction, the disc was observed by opticalmicroscopy on a Dialux 20 (Leitz, Solms, Germany). Anysacrificed disc was replaced by a clean disc in order tomaintain the total area available for biofilm growthconstant.

Visualization

For biomass monitoring, the axis bearing the discs wasregularly dismounted from the system. Each disc backsidewas carefully cleaned as only the biofilm developing on thefront was monitored. Each disc was deposited in a chamberwith a transparent bottom (Fig. 2a) and was quickly scannedin light transmission mode on a flat-bed scanner (Epson4490) with a spatial resolution of 315 pixels/cm (i.e.,800 dpi) and 256 gray levels (Fig. 2c) (im1 images). All thescanner settings were kept constant throughout the runs.The size of the im1 images is 3724� 3768 pixels. A blankimage of the chamber was taken at the beginning of eachscanning session to check the stability of the visualizationsystem with respect to time. The scanning system wasconsidered stable as the coefficient of variation of thevisualization chamber average gray level for all experimentswas 0.4%. The spatial homogeneity of the illumination inthe visualization chamber was assessed by the coefficient ofvariation of its gray level histogram, which was equal to1.4%.

Image Analysis

The first step of the image analysis is the detection of the discborder. Three points (Mi, with i¼ 1–3) are pointed outwith the mouse by the operator on the border and theircoordinates (xi, yi) are saved.

Figure 2. Images of the chamber without disc (a), of a blank disc (b) and of a

disc with biofilm (c).

The coordinates xc0 and yc0 of the center of the disc and itsradius R are given by the following relations:

yc0 ¼ c1 � a1ðc2=a2Þb1 � b2ða1=a2Þ

xc0 ¼ c2 � b2yc0

a2

R ¼ ½ðx1 � xc0Þ2 þ ðy1 � yc0Þ21=2

with a1¼�2x1þ 2x2, b1¼�2y1þ 2y2, c1 ¼ �x21 � y2

1þx2

2þy22, a2¼�2x1þ 2x3, b2¼�2y1þ 2y3, and c2 ¼ �x2

1�y2

1 þ x23 þ y2

3

To check the accuracy of the detection of the outer border,the average radius was computed for each run. If the errorwith respect to the nominal radius was larger than 1%, thelocalization procedure was repeated. A ring of interest isdefined by taking into account the gap between the axis ofrotation and the water line (biofilm develops only below thewater line). It corresponds to the wetted area of the disc. Thering limits were set to limlower¼ 35% and limupper ¼ 98% ofthe total radius.

Visilog 6.3 (Noesis, Les Ulis, France) and an in-houseFORTRAN-based program were used to determine the graylevel statistics (mean gray level GL

� �and its standard

deviation) in the ring and to transform the ring into arectangular image. This rectangular image, im2, correspond-ing to im1 has Na lines (corresponding to Na angles between0 and 2p in the original image) and Nr rows (correspondingto Nr radial positions between 0 and R in the original image;Fig. 3). The relation between the gray levels (GL) in imagesim1 and im2 is given by:

GLim2ði; jÞ ¼ GLim1ðii; jjÞwith

ii ¼ int i cos j2p

Na

� � R

Nrþ xc0

� �

and

jj ¼ int i sin j2p

Na

� � R

Nrþ yc0

� �:

i, j, ii, and jj are integers.Practically Na¼ 720 and Nr¼ 1,024. The value selected

for Nr allows us to keep about the same spatial resolution asin the original image: Dx¼Dy¼ 0.0317 mm/pixel againstDr¼ 0.0488 mm/pixel in the transformed image. For Na thechoice is more difficult but an angular increment of 0.58 wasfound reasonable, due to the discrete nature of images.

The biofilm development was assessed by the variations ofthe opacity, O, with respect to time t:

OðtÞ ¼ GLt¼0 � GLðtÞ:

Opacity can be evaluated directly on image im1 or onthe corresponding subset on image im2, im2sub¼ im2

Pons et al.: Biofilm Monitoring on Rotating Discs 107

Biotechnology and Bioengineering

Figure 3. Transformation of a disc image into a rectangular image.

(limlower ! limupper, Na). The textural descriptors definedby Haralick et al. (1973, Appendix) were evaluated on im2sub

by a FORTRAN-based program. These descriptors aregenerally computed for a set of directions (a) and distancesbetween pixels (d). p(i,j) represents the normalized numberof occurrences of having a pixel X of gray level i and a pixel Yof gray level j, for a specific position operator, defined bythe interpixel distance d and the angle a between X and Y(Fig. A of Supplementary Information). In the present casethe descriptors were calculated for a¼ 08 and a¼ 908, inorder to express the textural characteristics of the biofilmwith respect to the radial and angular directions on the discs.

It can be shown that the proposed transformation ensuresthe invariance of the texture descriptors with respect torotation. This is illustrated by the simple example of FigureBa of Supplementary Information, where a disc with a darkspot located off its center was rotated around its center.The effect of rotation is clearly seen on Figure Bb ofSupplementary Information where the Energy calculatedwithout the proposed transformation within the disc fora¼ 08 and d¼ 10 pixels varies with the angle of rotation.The coefficient of variation of Energy without transforma-tion reaches 3%. On the contrary, the radial and the angularEnergies, calculated with the proposed transformation ford¼ 10 pixels for both directions, are more stable withrespect to rotation, with coefficients of variation of 0.2% and1.4% respectively. It should be noted, however, that there isno direct relation between the values taken by the texturedescriptors without and with transformation.

Due to the large number of texture descriptors, theanalysis is further facilitated by the use of PrincipalComponents (Einax et al., 1997). The Principal Compo-nent Analysis was run on Scilab (Inria, Rocquencourt,France).


Results

Biofilm Development

The variation of opacity of the seven discs used in Run 7 isplotted in Figure 4. Run 7 is the longest experiment of theseries. Variations in the composition of the municipalwastewater feeding the reactor occurred during the test(Fig. C of Supplementary Information). In spite of thevariations observed in the composition of the wastewaterfeeding the reactor, nitrification occurred in the reactor asammonia was oxidized and nitrate produced, indicating thepresence of nitrifiers in the microbial consortium. After20 days, the residual ammonia concentration in the effluentwas below 2 mg N-NH4/L. The opacity increased on discsd1, d2, and d3 in a similar manner during the first 60 days, aswell as for the shorter runs of d4 and d5. Discs d4 and d5were sacrificed early in order to build the opacity versusbiomass dry weight curve. A steady increase of opacity wasalso observed for disc d6, which replaced disc d5. Also afterapproximately 60 days, opacity for d6 leveled off at a lowerlevel than on discs d1, d2, and d3. Between days 60 and 80the opacity and the standard deviation remained constantfor discs d1, d2, and d3, but on day 76 a 15% increase of thestandard deviation was observed for d1. This was due tohigher gray levels resulting from the accidental detachmentof a fragment of biofilm near the outer edge of the disc. Thisdetachment was aggravated a few days later (46% increase ofthe standard deviation), when the disc rotation stopped forabout 12 h due to a power failure. In the submerged part ofthe biofilm a large part of the biomass was lost. After day100, the standard deviation increased for discs d1, d2, d6,and d7 due to the loss of large fragments of biofilm on thewhole surface of the discs. The opacity of disc d2 decreases

Figure 4. Opacity versus time for the seven discs used in Run 7. Bar¼�one standard deviation. Note that the disks were not rotating from days 81 to 82. Disks d6 and d7 were

added to the reactor as listed in Table I.

due to this loss and was 27% lower than disc d1 opacity onday 104 (end of the experiment). No regrowth was observedon these discs after the loss took place. However the biofilmremained stable for disc d3.

Another example of heterogeneous biofilm developmentis shown in Figure 5 for Run 9. An increase of the standarddeviation is observed on disc d3 on day 35, which is followedby a slow loss of biomass. On day 49 severe biomass loss ofthe biofilm occurred on discs d1 and d4, but not on disc d3.Subsequent regrowth was observed, although the biofilmremained patchy: large standard deviations are due to the

increased range of gray levels. The general characteristicsof the influent wastewater in terms of soluble substanceswere constant during this period (Fig. D of SupplementaryInformation). However, the wastewater was sampled from agrit chamber, which was not working properly on day 49.It is possible that sand particles may have been present inthe feed on that day, inducing localized ripping off of thebiofilm.

In Figure 6 the development of the biofilm on disc d1during Run 4 is presented. At day 34, a severe changeoccurred with the switch from wastewater to synthetic feed.



Figure 5. Opacity of three discs during Run 9. Bar¼�one standard deviation. Disk d4 was added to the reactor as listed in Table I.

A decrease of the opacity and an increase of the standarddeviation indicate biofilm loss. However, after day 47,regrowth was observed, as demonstrated by the opacityincrease. The biofilm development on disc d3 was alsoaffected by the substrate change with a slight decrease inopacity for 4 days until opacity increases again. However, thebiofilm was younger as the disc was placed in the system onday 20 and its opacity much lower: on day 34 the opacity ofdisc d1 was more than three times the opacity of disc d3.

Finally a linear relationship (coefficient of correlationr2¼ 0.88) was obtained between total biomass and theaverage disc opacity for data points from all discs (Fig. 7).

Macrostructure Characterization

The selection of the distance d is a key issue in SGLDM. Itwas first verified that no large differences could be observed

Figure 6. Opacity of disc d1 during Run 4. Bar¼�standard deviation.


with respect to the choice of d, both for the radial (a¼ 08)and the angular (a¼ 908) directions. An example is given inFigure E of Supplementary Information for Energy. Smoothvariations in function of d are obtained for both angles,the radial direction being slightly more sensitive to d in thelower range than the angular direction. On one hand lowvalues of d are more sensitive to noise. On the other hand thenumber of occurrences that can be calculated is reduced forlarge values of d with respect to the image size, as both pixelsunder scrutiny has be within the image. In Figure 8 we givean example of the variations of four of the Haralick’sSGLDM descriptors, namely Energy, Contrast, Homogeneity,and Textural Entropy for disc d1 in Run 7. A rapid decreaseof Energy and Homogeneity is observed in the first 15 days ofthe biofilm development. This corresponds to the early

Figure 7. Correlation between biomass and opacity: Runs 4 (&), 5 (~), 6 (*), 7

(^), and 9 (^).

Figure 8. Variations of Energy, Contrast, Homogeneity and Entropy for disc d1 in Run 7. d¼ 50 (^), 100 (&), and 150 (~) pixels.

phase of biofilm development. These changes could not bedetected in measurements of opacity and its standarddeviation. An increase of Textural Entropy and Contrast isalso noticeable during this period. Then until day 76, thedescriptors remain constant. No large difference can be seenbetween the descriptors calculated for the different distances(d) and angles (a). This period corresponds to the regularand homogenous development of the biofilm, as observedwith opacity. On day 76 the values of Contrast for the threedifferent distances calculated for a¼ 08 start to deviate fromeach other. A similar behavior is observed for Homogeneity.This deviation is also reflected in the increase of the opacitystandard deviation due to the detachment of a fragment of

biofilm. Contrast reacts to the large biofilm detachment onday 100. Differences can be observed in the Contrast valuescomputed for d¼ 50, 100, and 150 pixels and a¼ 08 but notfor a¼ 908. In Figure 9, the four descriptors have beenplotted for discs d3 and d6 of Run 7 with a¼ 08. No losswas observed for disc d3 and, after the first 15 days, thedescriptors remain constant, describing a stable texture andmacrostructure. For disc d6, the detachment on day 100 canbe seen as an increase of Contrast. As for the discs which wereplaced in the reactor at time t¼ 0, the early phase of thebiofilm development can be monitored by a rapid decreaseof Homogeneity and Energy and a slight increase of TexturalEntropy.



Figure 9. Variations of Energy, Contrast, Homogeneity and Entropy for discs d3 and d6 in Run 7. a¼ 08. d¼ 50 (^), 100 (&), and 150 (~) pixels.

Four descriptors were chosen from the original set inorder to demonstrate how textural descriptors can be usedto monitor the biofilm macrostructure changes. However, itis difficult to select which descriptors are the more suitable.One option is to consider all of them. As the problemdimension is large, a convenient solution is to perform aPrincipal Component Analysis. The original descriptor setin Haralick et al. (1973) contains 26 descriptors values foreach of the 440 data points (13 values for a¼ 08 and 13values for a¼ 908, all computed for d¼ 100 pixels). InFigure 10a the texture trajectories of all the discs arerepresented in the plane of the first two PrincipalComponents. The first three Principal Components accountfor 55.9%, 18.5%, and 15.9% of the total variability,respectively. All the trajectories start from the same zonecorresponding to a PC1 value of 7.06 (with a coefficient of


variation of 4.3%), a PC2 value of 3.53 (with a coefficient ofvariation of 9.17%) and a PC3 value of 2.19 (with acoefficient of variation of 24%). In the first part of thetrajectory, both PC1 and PC2 decrease. This corresponds tothe phase of steady development of the biofilm. Then PC2increases, when PC1 continues to decrease. This turningpoint in the trajectories corresponds to the time whenbiofilm loss takes place. Regrowth, as for disc d4 of Run 9,can be seen with a new turning point in the trajectory(Fig. 10b).

Discussion

Opacity and its standard deviation are parameters that canbe easily calculated on gray level images to monitor the

Figure 10. Trajectories in the PC1-PC2 plane of the biofilm development on all

discs (a) and on disc d4 of Run 9 (b).

biofilm development on transparent support at a macroscaleand for long periods of time, with minimal interference ofthe operator with the system under study. Opacity wasfound to be linearly correlated with the biomass dry weight.Milferstedt et al. (2006) have also shown that gray levelcould also be correlated with biofilm thickness. In thepresent case it was not possible to assess the biofilmthickness reliably as it was found very difficult to detect thesubstratum upper surface. The correlation between totalbiomass and opacity could be improved by an increase of thenumber of data points. However, the number of discs in oursystem is limited and much lower than the number of slidesused in Milferstedt et al. (2006). In the present case theopacity analysis was conducted directly from the gray levelhistograms of the rings (images Im1) and not from the graylevel histograms of the rectangular images (images Im2). Itcould have been conducted indifferently on these images asgray-level histograms are not pixel location dependent.There are linear relationships between the average graylevels of Im1 and Im2 (slope¼ 1.02, r2 > 99) and betweenthe standard deviations of their gray level histogram

(slope¼ 1.00, r2> 0.99). The relation is only valid in therange for which the biofilm is sufficiently transparent. It isnot universal, as it depends upon the settings of the scannerand the properties of the biofilm.

A sudden increase of the opacity standard deviation is anindicator of biofilm loss. Biofilm loss is here used here as ageneric term encompassing effective biofilm detachment,autodigestion (in case of substrate limitation) or protozoangrazing. It was observed through the microscopic observa-tion at the end of the experiments that the biofilms werethe shelter of large numbers of protozoa and metazoan.No detailed species identification or quantification wasperformed but most of them behave as grazers althoughsome sessile protozoa could be observed. In the case of thechange from wastewater to a synthetic substrates, the opacitydecreased for both discs d1 and d2 in the next daysthen increased again: although the experiment needs to bereproduced, it could be hypothesize that the bacteria reactedto the substrate change by reducing initially their growthrate, which could not compensate anymore the destructionrate due to protozoa grazing. After a few days, the bacteria(or at least a fraction of them) got adapted and the overallbiofilm growth rate increased again.

The combination of monitoring standard deviationswith texture analysis provides more insight into the biofilmmacrostructure development in response to the variability ofoperation conditions such as feed rate and/or composition.Texture analysis gives information on the spatial organiza-tion of the gray levels, which cannot be assessed by simplegray level statistics. There are many techniques for textureanalysis from images. Most of them have been primarilydeveloped for aerial photographs (Ge et al., 2006) but theyhave found other applications as they can be applied to anytype of images (see for example, Cheng and Cui, 2006 in themedical field or Venkat Ramana and Ramamoorthy, 1996for machined surfaces). The only requirement is that thepixel neighborhood is of the square grid type. In the presentcase, as discs are used as support medium, a transformationwas applied to map the circular geometry into a rectangularone, on which the SGLDM method could be used. The use oftwo directions (08 and 908) for the descriptors’ calculationallow the detection of spatial organization such as circularripples of higher biomass density which would be translatedinto vertical lines on the transformed images. We could notdetect the development of ripple structure in our experi-ments.

The selection of the distance d is always a topic ofdiscussion in SGLDM applications. In their work ofclassification of froth structures, Moolman et al. (1995)used a value of 5 pixels, which represented between 1% and8% of the image size. In order to encompass the variabilitywith respect to d, Gelzinis et al. (2007) proposed to fitpolynomials of degree 2–3 to the coefficients computedfrom co-occurrence matrices calculated for several values ofd. In the present work three values were used for d: 50 pixels(2.44 mm for a¼ 08 and 258 for a¼ 908), 100 pixels(4.88 mm for a¼ 08 and 508 for a¼ 908) and 150 pixels



(7.32 mm for a¼ 08 and 758 for a¼ 908). This correspondsto 5–15% for the radial direction and 7–21% in the angulardirection.

By using individual texture descriptors, such as Energyor Homogeneity or by combining them in a PrincipalComponent Analysis approach, different features of thebiofilm development could be identified. The early phase ofbiofilm development could be monitored. During thisperiod, the variations in opacity are small but their effect onthe neighborhood characteristics is strong. During the phaseof steady development of the biofilm, the texture descriptorsare globally stable, indicating a stable macrostructure.During this period, opacity is useful to monitor the biofilm.In Figures 8 and 9, slight differences can be observed interms of homogeneity in function of the distance d along thea¼ 08 direction between days 60 and 80 for discs d1 and d3,respectively. These differences cannot be seen for a¼ 908.This tells us that changes in the macrostructure are detectedalong the radial direction (corresponding to a¼ 08) for thetested d values but not along the angular direction. Then, assoon as an event causes any biofilm detachment, the texturedescriptors respond quickly to the change in the macro-structure. Events occurring (voluntarily or involuntarily) inthe present work (such as presence of sand-like particles,change of the feed composition, or power failure causingrotation interruption) had an effect on the biofilmdevelopment. An example of the benefits given by a textureanalysis is given in Figure 11 with discs d2 and d3 duringRun 7. On days 73 and 104, d3 presents opacities within 10%variation (143 and 158, respectively), standard deviations(15.9 for both) but very different Energies (5� 10�4 and

Figure 11. Images of discs d2 and d3 on days 73 and 104 during Run 7.


11� 10�4 respectively) and Contrasts (320 and 434respectively). The descriptors were calculated along theradial direction for d¼ 100. The large void zone on theNorth-East sector of d3 at day 73 has been recolonized onday 104, but other voids zones have appeared at differentplaces. The global opacity is similar but the relative positionof pixels with different gray level has changed. The situationis very different for disc d2. On day 73, its characteristics areclose to those of disc d3: 138 for opacity, 13.9 for standarddeviation, 6.1� 10�4 for Energy and 241 for Contrast. Butthey are very different on day 104 (97.6 for opacity, 67 forstandard deviation, 2.1� 10�4 for Energy and 4,522 forContrast) due to the massive detachment which took placeon that disc. Although d2 and d3 were run in parallel, thebiofilms experienced very different fates, for which we donot have any clear clues to this moment. It is expected thatrunning experiments with a detailed monitoring of themacrostructure will help to understand better macrostruc-ture changes and to predict the biofilm fate in response tovarious stresses. The different visual textures obtained afterthe various events led to different sets of texture descriptors.However, as the number of discs in our experimental setupis limited, more experiments are necessary to get a largerdatabase of the possible biofilm textures and macrostruc-tures, which could be encountered in such a system. It wouldbe interesting to see whether textures resulting from auto-digestion, or protozoan grazing could be differentiated fromeffective detachment events as these phenomena differ intheir origin: substrate limitation could be hypothesized forauto-digestion when detachment events could be due tohydrodynamic stresses. In several cases, regrowth of biofilmcould be observed after loss, with the trajectories of texturedescriptors turning back toward the zone corresponding tothe macrostructure state prior to the event, without howevera full return to this state.

Although the size of the discs used in the experimentsdiscussed in the present paper is small with respect to thesize of discs in a full-scale plant, larger discs (up to about20 cm) can be accommodated on the scanner. This couldallow investigating the effect of stresses due to differenthydrodynamical conditions between the center and theouter edge of the discs.

Conclusions

An image analysis procedure based on the assessment of graylevel distributions and textural characteristics has beendeveloped for circular supports used for biofilm develop-ment. The images can be used for the long-term monitoringof biofilm development using SGLDM and texture analysis.Using these tools the following conclusions can be drawn:

T
exture analysis based on SGLDM allows to discriminatebetween biofilms of different macrostructures such as thepresence of small or large areas void of biofilm. Average

muj ¼XNg

j pjðjÞ mj ¼XNg

j pjðjÞ
opacity and its standard deviation do not allow to analyzethe spatial structure.
j¼1 j¼1
A
m ¼mi þ mj

simple geometrical transformation allows circumvent-ing the potential problem of the support shape for textureassessment.

2
R
s2i ¼

XNg

i¼1

ði � miÞ2piðiÞ s2j ¼

XNg

j¼1

ðj � mjÞ2pjðjÞ

Energy ¼ f1 ¼XNg

i¼1

XNg

j¼1

pði; jÞ2

Contrast ¼ f2 ¼XNg�1

k2XNg XNg

pði; jÞ for ji � jj ¼ k

unning experiments with complex cultivation growthmedia such as wastewater did not hinder the imageanalysis procedure and interfere with the correlationbetween biomass and opacity.

This procedure can be used to study biofilm growth/regrowth phases as well as for the detection of discrete eventsof biofilm loss, which could be due to detachment, auto-digestion, or protozoan grazing, in a system mimicking thebehavior of rotating contactors for wastewater treatment, inorder to better understand their behavior.

k¼0 i¼1 j¼1

PNg PNg
Nomenclature
i¼1 i¼1

ðijÞ pði; jÞ � mimj
ai, bi, ci, i¼ 1, 2 parameter for disc center and radius determination
Correlation ¼ f3 ¼sisj
COD chemical oxygen demand
d
inter-pixel distance
Ng Ng
GL gray level
f ¼ s2 ¼XX

ði � mÞ2pði; jÞ
limlower, limupper lower and upper limits for biofilm area 4
i¼1 j¼1
Mi point on the disc border
Na
number of rows of Im2 images
N N
Nr number of lines of Im2 images Xg Xg
O
opacity pnðnÞ ¼
i¼1 j¼1

pði; jÞ for i þ j ¼ n
p normalized number of occurrences
R
disc radius XNg�1 r coefficient of correlation
mk ¼ k pkðkÞ for ji � jj ¼ k
SGLDM spatial gray-level dependence matrix
k¼0
t time
xc0
XNg XNgpði; jÞ
horizontal coordinate of the disc center (along image

lines)
Homogeneity ¼ f5 ¼
1 þ ði � jÞ2
xi horizontal coordinate of Mi (along image lines) i¼1 j¼1
yc0
vertical coordinate of the disc center (along image rows)
yi
vertical coordinate of Mi (along image rows) X2Ng a angle f6 ¼ mn ¼
n¼2

npnðnÞ for i þ j ¼ n

f7 ¼ s2n ¼

X2Ng

n¼2

ðn � mnÞ2pnðnÞ for i þ j ¼ n

The authors are thankful for financial support from the CNRS/UIUC

cooperation program, a CAREER award to Eberhard Morgenroth

from the National Science Foundation under grant No. BES-0134104

and to The WaterCAMPWS, a Science and Technology Center of

Advanced Materials for the Purification of Water with Systems under

the National Science Foundation agreement No. CTS-0120978.

f8 ¼ �X2Ng

n¼2

pnðnÞ log10pnðnÞ for i þ j ¼ n

Textural Entropy ¼ TEi;j ¼ f9 ¼ �XNg

i¼1

XNg

j¼1

pði; jÞlog10pði; jÞ

f10 ¼ s2k ¼

XNg�1

k¼0

ðk � mkÞ2pkðkÞ for ji � jj ¼ k

Appendix: Haralick’s 14 Textural Descriptorsfor the SGLDM Approach (Haralick, 1979;Haralick et al., 1973)

piðiÞ ¼XNg

j¼1

pði; jÞ pjðjÞ ¼XNg

i¼1

pði; jÞ



Difference Entropy ¼ f11 ¼ �XNg�1

k¼0

pkðkÞ log10pkðkÞ

f12 ¼ TEi;j � TEHXY1

maxðTEi; TEjÞ

with

TEHXY1 ¼ �XNg

i¼1

XNg

j¼1

pði; jÞ log10ðpiðiÞ pjðjÞÞ

TEi ¼ �XNg

i¼1

piðiÞ log10piðiÞ

and

TEj ¼ �XNg

j¼1

pjðjÞ log10pjðjÞ

f13 ¼ ð1 � exp½�2ðTEHXY2 � TEi;jÞÞ1=2

with

TEHXY2 ¼ �XNg

i¼1

XNg

j¼1

piðiÞ pjðjÞ log10ðpiðiÞ pjðjÞÞ

Pmax ¼ f14 ¼ pði; jÞmax

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