textural characteristics of five microorganisms

7/27/2019 Textural Characteristics of Five Microorganisms

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TEXTURAL CHARACTERISTICS OF FIVE MICROORGANISMS

FOR RAPID DETECTION USING IMAGE PROCESSING

S. KUMAR and G.S. MITTAL1

School of EngineeringUniversity of Guelph

Guelph, Ontario, Canada N1G 2W1

Accepted for Publication October 4, 2007

ABSTRACT

A rapid and cost-effective technique for identification and classificationof microorganisms was explored using fluorescence microscopy and image

analysis. After staining the microorganisms with fluorescent dyes (diamidino-

2-phenyl-indole [DAPI] and acridine orange [AO], images of the microor-

ganisms were captured using a charge-coupled device camera attached to a

light microscope. Textural features were extracted from the images. Fluores-

cence emission from Bacillus thuringiensis is the highest compared with othermicrobes, and the emission from Lactobacillus brevis is the lowest. Variousmicroorganisms can be differentiated using various textural features from

images using AO or DAPI dye. Many textural features of the images obtainedfrom the two dyes were different.

PRACTICAL APPLICATIONS

Conventional microbial detection methods take considerable time and arelaborious. Rapid methods are required so that pathogens and spoilage micro-organisms in foods and water can be identified and counted in a much shorter

time. This work investigates image processing techniques particularly basedon textural properties of the images of microorganisms. Images of microor-ganisms in samples can be captured using light microscopes after concentrat-ing using centrifuge or membrane separation devices. This work will assist indeveloping a commercial method for rapid detection of microbes in foodsamples.

1 Corresponding author. TEL: 519-824-4120 ext. 52431; FAX: 519-836-0227; EMAIL: [email protected]

Journal of Food Process Engineering 32 (2009) 126143. All Rights Reserved.

Copyright the Authors

Journal Compilation 2008 Wiley Periodicals, Inc.

DOI: 10.1111/j.1745-4530.2007.00207.x

126


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INTRODUCTION

Diseases caused by foodborne pathogens are one of the major causes ofdeaths in many countries. Every year, around 81 million people are affected byfoodborne diseases in the U.S.A. alone, costing the U.S. economy around$810 billion (Swaminathan and Feng 1994). The main pathogens causingfoodborne diseases are Escherichia coli, Salmonella sp., Listeria monocyto-genes, Staphylococcus aureus, Campylobacter jejuni, Bacillus anthracis,

Bacillus cereus, Clostridium botulinum and Clostridium perfringens. Tradi-tional methods of microbial detection are time-consuming and laborious.Because of this, more research is concentrated toward the development oftechnologies that can detect and identify pathogens in foods in a relativelyshort period of time.

Recent techniques for detecting microorganisms in foods include micros-copy and image analysis, fluorescence techniques (Errampalli et al. 1998;Fanatsu et al. 2002), flow cytometry (Veal et al. 2000; Cram 2002), spectro-scopic techniques (Ellis et al. 2002), light scattering approach (Nebeker et al.2001; Perkins and Squirrell 2002) and microbial detection using fiber optictechnology (Yu et al. 2002; Kramer and Lim 2004). These techniques havedrastically reduced the time required to detect microorganisms in foods.Moreover, the flow cytometer is a very expensive and complex instrument.However, this technique has a limitation that a small population of bacteriamay not provide a strong signal. Also, the immobilized surface can havenonselective adsorption which can give false signals (Rand et al. 2002).However, even if this technique is used for identification of microorganisms,there are problems of interference from unwanted samples.

Thus, these detection methods have their limitations and cannot be usedfor simple, rapid and inexpensive detection of multiple pathogens. However,microbial detection and classification can be carried out more effectively andrapidly by combining microscopic techniques with image analysis. Fluores-cence microscopy and image analysis have been combined to obtain thegeometrical properties of microorganisms. An image analysis procedure hasbeen developed (Schonholzer et al. 2002) for bacterial cells in foods. Thistechnique has been improved (Trujillo et al. 2001) by using a multiplanefocusing algorithm. Advantages of the confocal microscope have been elabo-rated over the light microscope (Takeuchi and Frank 2001) and have providedthe applications of confocal microscopy.

Most of the work using microscopy and image analysis has been con-ducted to enumerate and obtain the geometrical properties of the microorgan-isms. Geometrical parameters alone cannot be used for identification andclassification of microorganisms as microorganisms can have varied geo-metrical parameters in their growth phase. Also, the cells can be in random

IDENTIFICATION OF MICROORGANISMS USING IMAGE PROCESSING 127


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orientations on the slide when their images are captured (Huang 1999; Trujilloet al. 2001). Therefore, there is a requirement to incorporate other features inimage analysis. Hence, the objective of this study was to obtain texturalparameters using fluorescence microscopy and image analysis from images ofmicroorganisms stained with two dyes.

MATERIALS AND METHODS

Growth of Microorganisms

The microorganisms used in this study are Bacillus thuringiensis (C399),Escherichia coli K12 (American Type Culture collection 10798), Lactobacil-lus brevis (LJH240), Listeria innocua (C366) and Staphylococcus epidermidis(LJH343). These microorganisms were obtained from the Canadian ResearchInstitute for Food Safety, University of Guelph, Guelph, Canada.

The broth (250 mL) as a nutrient medium for growing the particularmicroorganism was taken in a sterile flask and autoclaved at 121C for 15 min.The medium was then cooled at room temperature. The media used to groweach individual microorganism were brainheart infusion for L. innocua, De

ManRogosaSharpe broth for L. brevis and tryptic soy broth for the otherthree microbes. All the media were purchased from Fischer Scientific (Ottawa,Ontario, Canada). Using a sterile loop, a single colony of each microorganismwas inoculated into the respective medium and incubated overnight at 37C.After growing the cultures for each microorganism, 5 mL from each of the fivecultures was taken in five separate 15-mL centrifuge tubes and was centrifugedusing a centrifuge (model J2 MC, Beckman Coulter, Fullerton, CA). Thespecifications of the centrifuge were rotor type = J.20.1, speed = 6,000 rpm,time = 10 min and temperature = 4C. After centrifugation, the cell pellet for

each of the five microorganisms was obtained by removing the supernatant.The cell pellets were washed twice by 1 mL of phosphate-buffered saline(PBS). After washing, the cell pellets were resuspended in 5 mL of PBS andvortexed thoroughly. Serial dilutions were prepared by transferring 1 mL fromeach dilution into 9 mL of PBS to make subsequent dilutions.

Fluorescent Staining

Two fluorescent dyes, diamidino-2-phenyl-indole (DAPI) and acridineorange (AO), were purchased from Sigma-Aldrich (Oakville, Ontario,Canada). These dyes were selected as the filter cubes available with themicroscope had the appropriate filters for excitation and photon emission ofthese dyes. Two milligrams of each dye was taken in 1 mL of phosphate bufferand was vortexed thoroughly. Serial dilutions of the dye solution were made by

128 S. KUMAR and G.S. MITTAL


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transferring 0.1 mL of the dye in phosphate buffer to 0.9 mL of phosphatebuffer. The third dilution of each dye was used for staining.

One milliter of aliquot of the cells suspended in PBS was passed throughpolycarbonate filters (AMD Lovingston, Mississauga, Ontario, Canada) with apore size of 0.4 mm. After the cells were collected on the filter paper, 0.1 mLof DAPI solution was used to stain the microorganisms collected on the filterpaper for 5 min. To prevent photobleaching and fading, antifading agentMowiol (VWR Canlab, Mississauga, Ontario, Canada) was added and thefilter paper was allowed to dry for 5 min.

For fluorescent staining using AO, the aforementioned steps were used.However, AO did not require antifading agents as it did not show photobleach-ing or fading during image capturing. After drying, the filter paper holding thestained microorganisms was transferred to a glass slide and covered with acoverslip. Immersion oil (fluorescence free) was applied between the filterpaper and the coverslip, and on the top of the coverslip.

Image Capturing

The system for image capturing and analysis consisted of an OlympusBX 60 reflected light fluorescence microscope (Carsen Group Inc., Markham,Ontario, Canada) fitted with a 12-V, 100-W halogen lamp. An air-cooledSensys charged-coupled device camera system was attached to the micro-scope. Image-Pro Plus (Media Cybernetics, Silver Spring, MD) was used forimage capturing, processing and analysis.

A filter cube consisted of exciter filters, barrier filters and dichroicmirrors. The excitation lights used for exciting the fluorophores from thesample were UM = common to all cubes, U = ultraviolet, V = violet, B = blue,IB = interference blue, BV = blue violet, W = wide band, G = green,IG = interference green and IY = interference yellow. The excitation wave-length for AO is 490 nm, and its emission wavelength is 530 nm (Abramowitzand Davidson 2000). Therefore, the filter cube used for AO was MWIB. Theexcitation filter was a band pass filter between 460 and 490 nm. The barrierfilter used in this cube was BA 515. This is a long pass filter that passes allemissions greater than 515 nm. The dichroic mirror used in this cube was DM500. The excitation wavelength for DAPI was 372 nm and its emission wave-length was 456 nm (Abramowitz and Davidson 2000). Therefore, the filtercube used for DAPI was UMWU. The excitation filter was a band pass filterbetween 330 and 385 nm. The barrier filter used in this cube was BA 420. Thisis a long pass filter that passes all emissions greater than 420 nm. The dichroicmirror used in this cube was DM 400.

The slide was placed on the stage of the light microscope. The micro-scope was set into the reflectance fluorescence mode. Different light intensity



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levels were tested and the illumination was the best when light intensity levelwas set to 10 V and a 100 oil objective was selected. Image capturing withvarious exposure times (10200 ms for AO dye, and 500660 ms for DAPIdye) was conducted. The best exposure time where high-contrast images forthe microorganisms were obtained clearly was selected (160 ms for AO dyeand 620 ms for DAPI dye). Images were captured by randomly moving thefield of view to different areas. The images captured were gray-scaleimages.

Image Preprocessing

The captured images of the microorganisms were preprocessed beforeusing them to extract their textural parameters. Background correction wasused to compensate for the uneven background intensities and to compensatefor irregularities due to uneven lighting, nonuniform camera response andminor optic imperfections. The background image was obtained withoutthe sample. During background correction, the active image was compared tothe background image, and the pixels that were considered to be part of thebackground were replaced by the values close to the mean background inten-sity. The algorithm for background correction used the following formula

(Image-Pro Plus, Media Cybernetics):

CI I BI M x y x y x y, , ,= + (1)

where Ix,y is the pixel value of the original image at location (x,y); BIx,y is thepixel value of the background image at location (x,y); M is the average pixelvalue of the background image, and CIx,y is the new pixel value in the correctedimage.

After the images were captured, each individual image of the cell forthe microorganisms was isolated and magnified to 200%. The images wherethe two cells were joined together were split using the split function of theImage-Pro Plus (Media Cybernetics).

Extracting Textural Parameters of the Images

Using the MaZda software (Institute of Electronics, Technical Universityof Lodz, Lodz, Poland), the following algorithms for textural analysis wereimplemented: moments of histogram, co-occurrence matrix, run length algo-rithm and discrete wavelet transform. Using the gray-level histogram, thefollowing features were obtained: variance of the histogram, smoothness,skewness and kurtosis. The co-occurrence matrix (Haralick et al. 1973)was computed for a distance of 1 pixel and an angle of 0. All the images



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were eight-bit gray-scale images. The following texture features wereobtained using the co-occurrence matrix: angular second moment (ASM)(homogeneity), contrast, sum of squares, sum average, sum variance (hetero-geneity), entropy and sum entropy. Using the run length algorithm in theMaZda software (Institute of Electronics, Technical University of Lodz), thefollowing parameters were obtained: run length nonuniformity, gray-levelnonuniformity, long-run emphasis, and short-run emphasis.

Using discrete wavelet transform, the image was broken into four sub-band images after passing through a cascade of low and high pass filters.The sub-band images were lowlow (LL), lowhigh (LH), highlow (HL)and highhigh (HH). The HH subimage represented the diagonal details. HLrepresented the horizontal high frequencies. LH gave vertical high frequen-cies, and LL gave the low-frequency details (Materka and Strzelecki 1998).Wavelet energy was calculated for each sub-band image. The LL sub-bandimage was further decomposed into the four sub-bands at the next level. Theresulting image from the decomposition of the LL sub-band image gavefour more images at LL, LH, HL and HH sub-bands. Wavelet energy wascalculated for each sub-band image at the second level. The process wasrepeated to obtain wavelet energy for each sub-band image at the thirdlevel also.

Duncans multiple range test for comparing the mean textural parametersfor the microorganisms was conducted using SAS v. 8.02 (SAS 2002).

RESULTS AND DISCUSSION

The cells stained with AO appeared orange in color. Most of the cells ofB. thuringiensis appeared single and did not form chains (Fig. 1). E. coli wasalso observed as long and wide rods. However, E. coli was smaller in lengthcompared with B. thuringiensis. L. innocua appeared as small rods. L. brevisappeared as small coccoid rods. They appeared either singly or formed chains.S. epidermidis cells appeared spherical. DAPI-stained cells appeared lightblue in color using the WU filter and UV excitation. Images of E. coli hadhigher gray-level intensity as compared with the images of the remainingmicroorganisms (Fig. 2).

Texture Features Using Moments of Gray-level Histogram

Cells Stained with AO. The mean value of the second moment of thegray-level histogram for B. thuringiensis was the highest (Table 1), indicatingthat the pixels forming the images ofB. thuringiensis have a large variation intheir gray-level values compared to the pixels forming the images of other



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microorganisms. High values of this parameter gave the smoothness parametervalue more closely to 1, indicating more roughness. Therefore, the surface of

B. thuringiensis is the roughest among the microorganisms tested. This param-eter was of the same range for E. coli, L. innocua and L. brevis, and the lowestfor S. epidermidis. B. thuringiensis, S. epidermidis and E. coli can be separatedfrom the remaining microorganisms using this parameter.

Cells Stained with DAPI

Duncans multiple range test shows that the mean second moment of thegray-level histogram for the images of E. coli was the largest (Table 1). Thevalue of the smoothness parameter for the images of E. coli was closer to 1than for the other microorganisms. Hence, the surface of E. coli was theroughest. This parameter for S. epidermidis was the lowest. Thus, E. coli canbe separated from the remaining microorganisms using this parameter.

FIG. 1. IMAGES OF MICROORGANISMS STAINED WITH ACRIDINE ORANGE



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Extraction of Textural Parameters Using Co-occurrence Matrix

The co-occurrence matrix takes into account the spatial locations as wellas the gray-level information for the pixels to calculate the textural parameters(Sonka et al. 1998). Only the results for the co-occurrence matrix for angle 0are discussed.

Microorganisms Stained with AO

Table 2 shows the range, mean and SD of the textural parameters calcu-lated using the co-occurrence matrix.

ASM represents the homogeneity of the surface. Large values of ASMrepresent a more homogeneous surface (Haralicket al. 1973). ASM values forS. epidermidis and L. innocua were greater than the ASM values of othermicrobes. Therefore, the textures of S. epidermidis and L. innocua are morehomogeneous compared with the textures of L. brevis and E. coli. The ASM

A Bacillus thurigiensis B E. coli K12

C Listeria innocua D Lactobacillus brevis

E Staphylococcus epidermis

FIG. 2. IMAGES OF MICROORGANISMS STAINED WITH DIAMIDINO-2-PHENYL-INDOLE



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value for B. thuringiensis was the minimum. Thus, the texture of B. thuring-iensis has the least homogeneity. ASM values for B. thuringiensis and E. coliwere significantly different. Similarly, the ASM values for S. epidermidis and

L. brevis were significantly different. Moreover, B. thuringiensis and E. colicould be differentiated from L. brevis and S. epidermidis using their ASMvalues. The ASM value for L. innocua was significantly different from thevalues for L. brevis, E. coli and B. thuringiensis.

Using sum of squares, S. epidermidis could be differentiated from B.thuringiensis and E. coli. However, the sum of squares value for S. epidermidiswas significantly different from the remaining microorganisms; B. thuringien-sis was significantly different from the remaining microorganisms, and valuesfor E. coli, L. innocua and L. brevis were not significantly different.

Using sum average, B. thuringiensis can be differentiated from L. brevis,L. innocua and S. epidermidis. The value for E. coli was also significantlydifferent from the remaining microorganisms. Values for L. brevis, L. innocuaand S. epidermidis overlapped.

The entropy from B. thuringiensis images was the highest, indicating themaximum complexity in the images. This was followed by the value forE. coli.The entropy values forL. brevis,L. innocua and S. epidermidis were lower thanfor B. thuringiensis and E. coli. The entropy values for B. thuringiensis and E.coli were significantly different (Table 2). Thus,B. thuringiensis andE. coli canbe differentiated from L. brevis and S. epidermidis using the entropy values.

TABLE 1.

RANGE, MEAN AND SD OF TEXTURAL PARAMETERS OBTAINED FROM MOMENTS OF

HISTOGRAM FROM THE IMAGES OF MICROORGANISMS STAINED WITH AO AND DAPI

Microorganism Second moment of histogram Smoothness

AO DAPI AO DAPI

Bacillus

thuringiensis

Range 200874 22105 0.9950.998 0.9640.990

Mean/SD 437 172a 57 26b 0.997 0.001a 0.978 0.001b

Lactobacillus brevis Range 60174 1196 0.9830.994 0.9210.988

Mean/SD 107 40b 53 28b 0.989 0.002b 0.973 0.020b

Listeria innocua Range 70180 2091 0.9860.994 0.9740.986

Mean/SD 134 35b 57 18b 0.992 0.002b 0.980 0.007b

Escherichia coli K12 Range 85185 110472 0.9880.994 0.9910.996

Mean/SD 130 32b 241 113a 0.991 0.002b 0.995 0.002a

Staphylococcus

epidermidis

Range 3075 1380 0.9670.986 0.9290.986

Mean/SD 56 14c 37 15c 0.981 0.005c 0.969 0.012c

n = number of images = 34.

Means with the same letter in a column are not significantly different at 95% level.

DAPI, diamidino-2-phenyl-indole; AO, acridine orange.



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TABLE2

.

THERAN

GE,MEANANDSDOFTEXTURALPARAMETERSCALCUL

ATEDUSINGCO-OCCURREN

CEMATRIXFROM

IMAGESO

F

MI

CROORGANISMSSTAINEDW

ITHACRIDINEORANGE

Microorganism

A

SM

Sumofsquares

Sumaverage

Entropy

Sumentropy

Sumva

riance

Bacillusthurin

giensis

Range

0

.0120.025

120728

200340

1.61.9

1.41.7

4002

200

Mean

0

.017d

375a

266a

1.86a

1.64a

1320a

SD

0

.003

188

39

0.08

0.07

604

Lactobacillusbrevis

Range

0

.0280.049

39208

120181

1.31.5

1.01.3

1206

89

Mean

0

.037b

99b

155c

1.49c

1.27c

352b

SD

0

.047

44

13

0.07

0.08

153

Listeriainnocua

Range

0

.0200.05

40220

120184

1.21.7

1.11.4

1006

50

Mean

0

.041a

101b

153c

1.48c

1.28c

339b

SD

0

.014

37

20

0.17

0.13

139

Escherichiaco

liK12

Range

0

.0150.030

80296

140220

1.61.9

1.31.6

2506

90

Mean

0

.022c

133b

181b

1.77b

1.51b

458b

SD

0

.004

51

25

0.09

0.09

153

Staphylococcu

sepidermidis

Range

0

.0340.051

1977

130190

1.31.5

1.11.3

503

00

Mean

0

.041a

51c

158c

1.47c

1.24c

173c

SD

0

.007

19

15

0.06

0.07

76

n=

numberof

images=

34.

Meanswithth

esameletterinacolumnarenot

significantlydifferentat95%level.

ASM,angular

secondmoment.



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The sum entropy for B. thuringiensis was the maximum followed by thevalue for E. coli. The sum entropy ofL. innocua, L. brevis and S. epidermidiswas the minimum without any significant differences. Therefore, the degree ofrandomness in the images of B. thuringiensis was the maximum. Using sumentropy, B. thuringiensis can be differentiated from L. brevis, L. innocua andS. epidermidis. The sum entropy values for B. thuringiensis and E. coli weresignificantly different.

Using sum variance, only B. thuringiensis and S. epidermidis can bedistinguished from other microorganisms. The sum variance values for E. coli,

L. brevis and L. innocua were not significantly different.

Images of Microorganisms Stained with DAPI

Table 3 shows the range, mean and SD of the textural parametersobtained using the co-occurrence matrix from the images of microorganismsstained with DAPI. These are discussed next.

L. brevis and S. epidermidis had higher mean ASM values and thuswere more homogeneous than other microorganisms. They were followed by

L. innocua, B. thuringiensis and E. coli. The ASM values for B. thuringiensisand E. coli were the lowest and were not significantly different. Using thisparameter, B. thuringiensis and E. coli can be separated from L. brevis andS. epidermidis. The ASM of L. innocua was significantly different from theother microorganisms.

Using sum of squares, only E. coli can be differentiated from othermicroorganisms. The values for the remaining microorganisms overlapped.Using sum average, L. brevis can be differentiated from B. thuringiensis,

E. coli and S. epidermidis. Values for L. brevis and L. innocua were signifi-cantly different (Table 3). The value for E. coli was significantly different fromthe other microorganisms. The values for B. thuringiensis, L. innocua andS. epidermidis were not significantly different.

Images ofE. coli andB. thuringiensis have the highest mean entropy valuesshowing more complexity in their images. The entropy values for L. brevis andS. epidermidis were the lowest without significant differences. The entropyvalues for B. thuringiensis and E. coli overlapped. Using entropy, B. thuring-iensis and E. coli can be differentiated from L. brevis and S. epidermidis. Theentropy for L. innocua was significantly different from other microorganisms.

The sum entropy values for E. coli and B. thuringiensis were the highestwithout significant differences between them. The sum entropy for L. breviswas the lowest. The sum entropy values for L. innocua, S. epidermidis and

L. brevis were significantly different. Using sum entropy, B. thuringiensis andE. coli can be differentiated from L. brevis and S. epidermidis. The sumentropy for L. innocua was significantly different from other microorganisms.



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TABLE3

.

THERANGE,MEANANDSDOFTEXTU

RALPARAMETERSOBTAINE

DUSINGCO-OCCURRENCEMATRIXFROMTHEIMAGESOF

MICROO

RGANISMSSTAINEDWITHD

IAMIDINO-2-PHENYL-INDOLE

Microorganism

A

SM

Sumofsquares

Sumaverage

Entropy

Sumentropy

Sumva

riance

Bacillusthurin

giensis

Range

0

.0180.033

20110

185232

1.51.7

1.31.5

70450

Mean

0

.026c

56b

207b

1.68a

1.45a

213b

SD

0

.005

30

17

0.08

0.08

126

Lactobacillusbrevis

Range

0

.040.065

1395

150181

1.21.4

0.91.2

20240

Mean

0

.051a

44b

168c

1.38c

1.14d

125b

SD

0

.009

23

10

0.08

0.11

67

Listeriainnocua

Range

0

.0250.048

3099

170230

1.41.6

1.11.4

60260

Mean

0

.035b

60b

206b

1.55b

1.30b

173b

SD

0

.007

20

19

0.07

0.10

62

Escherichiaco

liK12

Range

0

.0150.033

120340

205265

1.51.9

1.31.6

2751,500

Mean

0

.023c

212a

236a

1.72a

1.47a

739a

SD

0

.006

73

20

0.14

0.11

367

Staphylococcu

sepidermidis

Range

0

.040.06

879

185239

1.31.4

1.11.2

20260

Mean

0

.049a

41b

207b

1.41c

1.19c

138b

SD

0

.006

23

16

0.05

0.05

80

n=

numberof

images=

34.

Meanswithth



ASM,angular

secondmoment.



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Using sum variance, E. coli can be differentiated from L. brevis,L. innocua and S. epidermidis. The sum variance values for B. thuringiensis,L. brevis, L. innocua and S. epidermidis were not significantly different.

Extraction of Textural Parameters Using Run Length Algorithm

Images of Microorganisms Stained with AO. Only run length nonuni-formity was able to distinguish some of the microorganisms; therefore, theresults are shown for run length nonuniformity at 0, 45 and 90 in Table 4.These are discussed next.

Low values of horizontal run length nonuniformity indicate that thelength of runs having the same gray-level values in the horizontal direction aremore or less similar throughout the image (Galloway 1975). The mean valuesfor S. epidermidis and L. brevis were lower than for other microbes. Therefore,the run lengths having the same gray-level values in the horizontal directionwere more similar for S. epidermidis and L. brevis than for the others.

B. thuringiensis has the maximum value. The values for B. thuringiensis andE. coli were significantly different. Using this parameter, B. thuringiensis andE. coli can be differentiated from the others. The value for L. innocua was

significantly different from that of L. brevis and S. epidermidis.The 45 run length nonuniformity is the measure of the run lengthnonuniformity at an angle of 45. The mean values for S. epidermidisandL. brevis were lower than for the others. The values forB. thuringiensis and

E. coli were significantly different. Using this parameter, B. thuringiensis andE. coli can be differentiated from the others. The mean value for L. innocuawas significantly different from the values for L. brevis and S. epidermidis.

Vertical run length nonuniformity is the measure of the run length non-uniformity in the vertical direction. The values for images of S. epidermidis

and L. brevis were lower than those of others. The values for B. thuringiensis,E. coli and L. innocua were significantly different. B. thuringiensis and E. colican be differentiated from L. brevis and S. epidermidis using this parameter.The value for L. innocua was significantly different from the values forS. epidermidis and L. brevis.

Images of Microorganisms Stained with DAPI

Table 4 shows the mean and SD of run length nonuniformity at 0, 45 and90 obtained from run length algorithm using the images of microorganismsstained with DAPI. These are discussed further.

S. epidermidis and L. brevis have the lowest mean values of horizontal runlength nonuniformity than other microorganisms. The value for B. thuringien-sis was the maximum. This trend is similar to the values of microorganisms



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TABLE4

.

THERANGE,MEANANDSDOFRUNL

ENGTHNONUNIFORMITYAT

0,45AND900OBTAINEDFR

OM

RUNLENGTHALGORITH

M

FROMIM

AGESOFMICROORGANISMS

STAINEDWITHAOANDDA

PI

Microorganism

Horizontalrunlengthnonuniformity

45runlengthnonuniformity

Verticalrunlengthnonuniformity

AO

DAPI

AO

DAPI

AO

DAPI

Bacillusthurin

giensis

Range

66

103

38123

86133

95165

4273

37118

Mean/SD

85

12a

80

23a

110

14a

130

23a

57

11a

75

24a

Lactobacillusbrevis

Range

17

35

1426

3553

2441

1830

1227

Mean/SD

27

4d

19

4d

46

5d

36

5e

25

4d

19

5d

Listeriainnocua

Range

20

43

1442

4565

3068

2045

1438

Mean/SD

30

7c

30

9c

56

6c

54

10c

32

8c

28

7c

Escheirchiaco

liK12

Range

45

68

3384

82105

75118

3063

3476

Mean/SD

56

7b

53

16b

94

6b

93

13b

48

10b

54

15b

Staphylococcu

sepidermidis

Range

19

29

1828

4153

3458

2027

1733

Mean/SD

25

3d

23

5d

46

3d

45

7d

25

2d

24

4d

Meanswithth



n=

numberof

images=

34.

DAPI,diamidino-2-phenyl-indole;AO,acridine

orange.



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stained with AO. Thus, horizontal run length nonuniformity differentiates themicroorganisms in the same way irrespective of using AO or DAPI dye.

L. brevis had the lowest mean run length nonuniformity at 45, andB. thuringiensis had the highest contrary to values for microorganisms stainedwith AO. For microorganisms stained with DAPI, the values for L. brevis andS. epidermidis were significantly different, and the value for L. brevis was thelowest. The parameter values for B. thuringiensis and E. coli were significantlydifferent. Using this parameter, B. thuringiensis and E. coli can be differenti-ated from L. brevis, L. innocua and S. epidermidis. The values were signifi-cantly different for L. brevis, L. innocua and S. epidermidis.

S. epidermidis and L. brevis have the lowest value of vertical run lengthnonuniformity than the other microorganisms. The value for B. thuringiensiswas the maximum. The vertical run length nonuniformity differentiates themicroorganisms in the same way irrespective of using AO or DAPI.

Texture Analysis Using Discrete Wavelet Transform

A similar trend was observed for the wavelet energies at the third level.Hence, only the wavelet energy for the LL sub-band images of the microor-ganisms at the first level was used to distinguish the microorganisms.

Images of Microorganisms Stained with AO

Table 5 shows that the wavelet energy for the LL sub-band images (low-frequency components of the image) for B. thuringiensis was the maximum.This parameter for E. coli, S. epidermidis, L. innocua and L. brevis was notsignificantly different. Using this parameter, B. thuringiensis could be differ-entiated from other microorganisms.

Images of Microorganisms Stained with DAPITable 5 shows that the mean wavelet energy for the LL sub-band image

for E. coli was the maximum. The values for B. thuringiensis, L. innocua andS. epidermidis were the same. The value for L. brevis was the minimum. Usingthis parameter, L. brevis can be differentiated from other microorganisms. Themean wavelet energy for E. coli was also significantly different from othermicroorganisms.

CONCLUSIONS

Many textural parameters extracted from images of microorganismsstained with AO are different from the parameters extracted from the



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images of microorganisms stained with DAPI. Thus, the identificationand classification of microorganisms using image analysis is stronglydependent on the fluorescent dye used. Parameters extracted from theimages stained with AO gave better results for classification of themicroorganisms.

Using AO dye, the second moment of gray-level histogram for B. thu-ringiensis was the highest, and it was same for E. coli, L. innocua and

L. brevis. When DAPI was used, it was the highest for E. coli. Similarly,using AO dye, the textures of S. epidermidis and L. innocua were morehomogeneous as compared with values for L. brevis and E. coli. Usingsum of squares, S. epidermidis can be differentiated from B. thuringiensisand E. coli. Using sum average, B. thuringiensis can be separated from

L. brevis, L. innocua and S. epidermidis. Further, B. thuringiensis and E. colican be differentiated from L. brevis and S. epidermidis using entropy. Sumentropy was the maximum for B. thuringiensis followed by E. coli. Sumvariance can distinguish B. thuringiensis and S. epidermidis from othermicrobes. B. thuringiensis provided the maximum horizontal run length non-uniformity. Using 45 run length nonuniformity, B. thuringiensis and E. colican be differentiated from other microbes. Vertical run length nonuniformityvalues for images of S. epidermidis and L. brevis were lower than othermicrobes.

TABLE 5.

RANGE, MEAN AND SD OF WAVELET ENERGY FOR THE LOWLOW SUB-BAND AT THE

FIRST LEVEL OBTAINED USING DISCRETE WAVELET TRANSFORM FROM IMAGES OFMICROORGANISMS STAINED WITH AO AND DAPI

Microorganisms Wavelet energy for the lowlow sub-band at the first level

AO DAPI

Bacillus thuringiensis Range 12,00028,000 8,00014,888

Mean/SD 18,626 4,974a 10,832 1,868b

Lactobacillus brevis Range 4,1008,200 4,0007,697

Mean/SD 6,498 1,366b 6,187 963c

Listeria innocua Range 4,4009,400 8,00013,000

Mean/SD 6,707 1,639b 10,800 2,390b

Escherichia coli K12 Range 7,50010,600 9,50023,072

Mean/SDD 8,937 1,074b 15,198 3,676a

Staphylococcus epidermidis Range 4,0008,200 8,00014,000

Mean/SD 6,772 1,631b 10,856 2,020b

n = number of images = 34.Means with the same letter in a column are not significantly different at 95% level.

DAPI, diamidino-2-phenyl-indole; AO, acridine orange.



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