floc segmentation

8/9/2019 Floc Segmentation

1/6

International Symposium on Green & Sustainable TechnologyISGST 2014, UTAR era! "ampus, #alaysia, Sept $0 % ct $, 2014

Segmentation and Detection of Activated Sludge Flocs in microscopic images

Muhammad Burhan Khan, Humaira Nisar, Ng Choon Aun, K.Chandrasekaran a/l Krishnan

Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampus Perak, Kampar, Malaysia

Keywords: Activated sludge, image processing, image segmentation

Abstract

Activated sludge process is commonly used in wastewater treatment plants to process domestic or industrial effluent. The mainobjects of interest in the activated sludge systems are flocs and the filamentous organisms. The proper settling of the sludgeflocs in the activated sludge wastewater treatment process is crucial to the normal functioning of the system. Sludge bulking

presents a common and persistent problem in wastewater treatment plants as it prevents the ability for flocs to settle down in

secondary clarifier of the plant. The conventional methods that detect this problem are time consuming and they give resultswhen very little time for precautionary measures is left. Hence image processing and analysis methods present potentialsolutions to the long standing problem. n this paper image processing techni!ues are used to segment and detect activatedsludge flocs in microscopic images. This can help in the study of the morphology of individual flocs and their !uantification.Segmentation is one of the most important part of digital image processing. "ood image segmentation is necessary for

identifying the objects of interest. n this paper, #tsu thresholding, $%means and &%means segmentation techni!ues are used tosegment and detect flocs in microscopic images of activated sludge. The performance of the segmentation techni!ues isevaluated for activated sludge images at different microscopic magnifications using global consistency error '"&() anddetection of number of flocs. "round truth images are used to benchmark the accuracy of segmentation algorithms.

Introduction

Activated sludge process is widely employed processincluded in the secondary stage of wastewater treatment

plants to treat wastewater from domestic sewage or foodindustry. The disturbances and abnormal state of Activatedsludge wastewater treatment process can be detected andidentified by morphology of flocs and filaments found inmi*ed li!uor samples from aeration basin of the plant'Heine, et al. +-) 'i/ukojc +0)'1es!uita, et al. +-2).

mage analysis has been e*tensively reported in the lastdecade in conte*t of morphology of flocs which has beencorrelated with sludge volume inde* 'S3), mi*ed li!uor suspended solids '14SS) and abnormal conditions like

filamentous bulking and pin flocs '1es!uita, et al. +--).mage segmentation is partitioning of the image into

significant regions which represent the objects of interest inthe image. A number of image segmentation techni!ueshave been reported in conte*t of activated sludge process.Histogram based thresholding using intermeans algorithmwas used by 5enne et al. for segmentation of flocs andfilaments '5enne6, et al. +2). Heine et al. employed image

enhancement and edge detection using gradient, followed by thresholding 'Heine, et al. +-). Sikora and Smolkareported that segmentation using analysis of low spatialfre!uency components and thresholding perform poorly because of irregular illumination 'Sikora and Smolka +-).

They also suggested floc segmentation using varianceoperator and filament segmentation using 4aplacian. Asanother alternative, Sikora and Smolka used &anny7s

algorithm followed by two level thresholding 'Sikora and

Smolka +-). The se!uence of background correction,histogram e!uali/ation, median filtering and morphologicaloperations was suggested by 8ere/ et al. for floc

segmentation '8ere/, et al. +9). 4ee et al. used watershedalgorithm for floc segmentation 'ong, et al. +-2). n this paper we have used #tsu thresholding, k%means and c%means segmentation for floc segmentation and evaluatedtheir performance for bright field microscopic images of sample collected from aeration tanks of domestic

wastewater treatment plants."iven a number of segmentation algorithms, it necessitatesa !ualitative or !uantitative assessment of the algorithms.Here we have adopted the "lobal &onsistency (rror '"&()

as performance metric because of its robustness to boundarymismatch. The appro*imation of ground truth images has

been prepared manually and segmentation of an image isassessed against respective ground truth using "&(.

Methodology

Samples are collected from an e*perimental setup and a

domestic wastewater treatment plant. A drop of sample wasanalysed under trinocular microscope with a coverslip.right field microscopy was used to ac!uire the imageswith resolution +;;


2/6


presented in the following paragraphs.

Otsu Thresholding

The #tsu thresholding method is a commonly usedthresholding techni!ue, suggested by #tsu '#tsu -=>=).

?ith this method, a threshold value dividing the intensityvalues into clusters@classes '+ for binary thresholding) that produce the ma*imum between%class varianceB is searched

for among all the available intensity values. The s!uare of total variance σ T of an image is the sum of s!uares of

within%class varianceB σ w

and between%class

varianceB σ b '#tsu -=>=).

σ T 2=σ w

2+σ b2

'-)The total variance is a constant number for any image, anda within%class variance that has a lowest value will causethe between%class variance to be ma*imal. t can be

imagined that a threshold which ma*imi/es σ b2

has

achieved an optimum clustering state, and hence this formsthe principle for the #tsu thresholding techni!ue. Thefollowing discriminant criterion 'of the three criteriasuggested by #tsu) was used '#tsu -=>=)

η=σ b2

σ T 2

'+)The optimum threshold will be the intensity at the inde*

where σ b2

is ma*imum.

K-means Segmentation

n k%means clustering, the objective is to partition the data'intensity values) into k clusters that minimi/es the within%

cluster sum of s!uares of (uclidean point%to%centroiddistance for all the clusters 'Tatiraju and 1ehta +;). Thealgorithm follows an iterative refinement techni!ue asfollows:

-. Cetermine@initiali/e k number of values for k number of clusters. They are known as cluster

centres.+. Attribute each data point with the cluster centre

closest to it.2. &alculate the mean of the data points within each

cluster. The means become the new cluster centres.D. Eepeat the above steps until convergence is

reached.

After convergence, the binary threshold value is to be takenfrom the border intensity value of the clusters.

Fuzzy C-means Segmentation

The fu//y c%means clustering assumes that every pi*el belong to each cluster to some e*tent specified bymembership function. The clustering is done iteratively tominimi/e sum of s!uare of point%to%centroid distance scaled by membership function, summed over all clusters 'e/dec-=;-). The algorithm flow is as follows:

-. nitial guess for cluster centres.+. Assignment of membership functions to every

point.

2. Fpdate of centroids and membership function.D. Eepeat from step + above until the centroids have avalue change not larger than the termination

tolerance.

Segmentation Assessment Criteria

There are a number of metric found in literature to assessthe segmentation. The parameter is chosen depending upon

the particular aspect 'such as si/e and shape etc) of theimage we are interested in. n order to assess thesegmentations in this paper, parameter of global consistency

error '"&() has been used because of its robustness for boundary errors. "&( can be effective metric of segmentation of performance as long as the segmentation

algorithms are not distorting the shape of the objects and arerobust to irregular illumination of microscopic images. "&(measured the consistency of segmentations with the goldstandard which was prepared by manual segmentation toappro*imate the ground truth."&( was suggested by 1artin '1artin, et al. +-) and is

based on the idea that if all segmentation are refinement of each other, "&( should have some minimum value. f segmentation does not appear to be refinement of the other,"&( should have higher value obviating that that the

segmentations are inconsistent. 4et S 1 and S 2 are twosegmentations and R(S,pi ) is some region in the

segmentation S, containing the pi*el pi. f n is the number of pi*els, the "&( is given by '1artin, et al. +-)

GCE=1

n min {∑

i

E ( S1 , S2, pi ) ,∑i

E(S2

, S1

, p i)}'-) and E (Sa , Sb , pi )=

| R ( Sa , pi )/ R ( Sb , p i )|| R (Sa , pi )|

'+)

where | x| is cardinality of set x and G is the difference.

Experimental Results

The results in this paper are based on initial version of our database which comprised of total 9 images: +9 at D*, -0at -*, -D at +* and 0 at D* magnification. The threesegmentation techni!ues which have been briefed in the previous section were implemented. A database of images

of sample collected from wastewater treatment plants wasconstructed at different objective magnifications of D*, -*,+* and D*. "round truth images were prepared for theimages in order to assess the accuracy of segmentation. Thesegmentation is evaluated against the respective ground

truth of each image. "lobal consistency error was used as

performance matric for the evaluation of the segmentationaccuracy. The results are depicted in the bar graph shown inthe figure.The two results were observed in two perspectives: performance of segmentation at each magnification and

effect of magnification on each segmentation algorithm. nthe first perspective, at each magnification, as obvious bythe figure -'a), #tsu thresholding segmentation gaveminimum "&( at each magnification. Then k%mean globalthresholding performed better than fu//y c%means at D* and-* magnification, but performed comparable with small

difference. t was observed that fu//y c%means and k%meanssegmentations are more sensitive to irregular illumination

than #tsu thresholding algorithm. llumination can alsoadversely affect the value of "&( as obvious from thetable. n the second perspective, fu//y c%means appreciably


3/6


performed better at D* magnification than -*, +* and D*

magnifications. At higher magnifications, "&( increases but the increase for fu//y c%means and k%meanssegmentations is more significant than #tsu. Actually #tsu performs better at D* magnification and appeared to beless sensitive to irregularity in illumination. The

observations are not true in absolute terms as the nature of

both illumination and segmentations are of stochasticnature. or e*ample, in the table -, at D* magnification, for the image 'b), "&( is greater for #tsu as compared to thatfor k%mean and fu//y c%means. Similar is the case for image

Table 1: Comparison of the segmentations gray!true positi"e# green!false positi"e# magenta!false negati"e$

1ag. #riginal mage "round Truth u//y c%means

segmentation

$%means

segmentation

#tsu Thresholding

D*

"&(I.0DD "&(I.0D+ "&(I.2=D

"&(I.>-+ "&(I.0=+ "&(I.;+0

-*

"&(I.D00 "&(I.D00 "&(I.209

"&(I.>>- "&(I.>>+ "&(I.>9D

+*

"&(I.-9-0 "&(I.-0+D "&(I.-+09

"&(I.->92 "&(I.->-0 "&(I.->+>

D*

"&(I.2->9 "&(I.2->2 "&(I.-2;+


4/6


"&(I.-+D; "&(I.-++; "&(I.;D>

Figure 1: %ean &C' for fuzzy (-means# )-means and Otsu thresholding segmentations at different magnifi(ations

Table *: Total number of flo(s dete(ted by fuzzy (-means# )-means and Otsu thresholding segmentations at different

magnifi(ations


5/6


%ag+

,umber of flo(s dete(ted

&round Truth Fuzzy (-mean K-means Otsu

Thresholding

D< -- ;> ;= ;=

-< -29 >+ >; --9

+< -D -+ - -9>

D< +- -+ -2 22

'a) at D* magnification, though #tsu performed best atD*. urthermore, there can be possibly two sources of error: irregular illumination and limited depth of field.inally, the segmentation techni!ues are compared on the basis of number of flocs detected. Here only those flocswere considered which contains more than = pi*els. All

the techni!ues, e*cept #tsu thresholding, detected lessnumber of flocs because some flocs are lost to the dark areas of irregular illumination. n case of #tsuThresholding, the techni!ue detected all the flocs in almostall cases, but with detection of additional flocs which are

actually the because of dark areas of irregular illumination.

Conclusions

The performance of segmentation algorithms is different for different microscopic magnifications. #tsu thresholdingsegmentation performed better as compared to fu//y c%mean and k%means segmentation. At low magnification, thethree algorithms work fine, but performance of fu//y c%means and k%means segmentation was deteriorated. 8re%

processing has not been included in this study to comparethe capability of the segmentation algorithms only. The

results obviate the e*tent of pre%processing re!uired for segmentation. The database of images has been constructedto include diverse illumination and field of depth conditionsto make the results consistent. As future work, database is

being e*panded to abnormal conditions of wastewater treatment plant, new segmentation techni!ues are beinginvestigated and additional assessment metric is needed tocater for irregular illumination.

Acno!ledgement

This work is supported by (Science Eesearch und grantfunded by 1inistry of Science Technology nnovation

'1#ST), 1alaysia '8roject Jo. 9%+%--%S-2=).

References

Heine, ?., Sekoulov, , urkhardt, H., ergen, 4., ehrendt,5.: (arly warning system for operation failures in biologicalstages of ??T8s by online image analysis. n : ?A&onference, erlin, pp.-0%-= '+-)

i/ukojc, (.: Application of image analysis techni!ues inactivated sludge wastewater treatment processes.iotechnology 4etters, -D+>%-D22 '+0)

1es!uita, C., Amaral, A., erreira, (.: Activated sludgecharacteri/ation through microscopy: A review on

!uantitative image analysis and chemometric techni!ues.Analytica &himica Acta, -D%+; '+-2)

1es!uita, C., Amaral, A., areira, (.: dentifying differenttypes of bulking in an activated sludge system through

!uantitative image analysis. &hemoshpere, 9D2%90+ '+--)

5enne6, E., anadda, (., 8hilips, J., mpe, 5.: mageanalysis as a monitoring tool for activated sludge propertiesin lab%scal installations. 5 (nviron Sci Health A To* Ha/ardSubst (nviron (ng., +=%-;. '+2)

Sikora, 1., Smolka, .: eature analysis of activated sludge based on microscopic images. n : &anadian conference on(lectrical and mputer (ngineering, pp.-2=%-2-D '+-)

8ere/, ., 4eite, S., &oelho, 1.: Activated sludgemorphology characterisation through an image analysis

procedure. ra/ilian 5ournal of &hemical (ngineering, 2-=% 22 '+9)

ong, 4., Jisar, H., eap, $.: An Approach for theSegmentation and Kuantification of Activated Sludge loclobs. Advanced Science 4etters '+-2)

#tsu, J.: A threshold selection method from grayscalehistograms. ((( Transactions on Systems, 1an and&ybernetics S1&%='-), 9+%99 '5anuary -=>=)

Tatiraju, S., 1ehta, A.: mage Segmentation using k%means

clustering, (1 and Jormali/ed &uts. Fniversity #f

&aliforniaLrvine '+;)

e/dec, 5. &.: 8attern recognition with fu//y objectivefunction algorithms.. 8lenum 8ress, Jew ork '-=;-)

1artin, C., owlkes, &., Tal, C., 1alik, 5.: A database of human segmented natural images and its application toevaluating segmentation algorithms and measuringecological statistics. n : (ighth ((( nternational&onference on &omputer 3ision, 3ancouver, & '+-)


6/6


floc segmentation

Documents