beef quality identification using thresholding method and ... · among these android related...

Research ArticleBeef Quality Identification Using Thresholding Method andDecision Tree Classification Based on Android Smartphone

Kusworo Adi1 Sri Pujiyanto2 Oky Dwi Nurhayati3 and Adi Pamungkas1

1Department of Physics Diponegoro University Semarang Indonesia2Department of Biology Diponegoro University Semarang Indonesia3Department of Computer System Diponegoro University Semarang Indonesia

Correspondence should be addressed to Kusworo Adi kusworoadifisikaundipacid

Received 11 April 2017 Revised 24 August 2017 Accepted 5 September 2017 Published 17 October 2017

Academic Editor Paolo Napoletano

Copyright copy 2017 Kusworo Adi et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Beef is one of the animal food products that have high nutrition because it contains carbohydrates proteins fats vitamins andminerals Therefore the quality of beef should be maintained so that consumers get good beef quality Determination of beefquality is commonly conducted visually by comparing the actual beef and reference pictures of each beef classThis process presentsweaknesses as it is subjective in nature and takes a considerable amount of time Therefore an automated system based on imageprocessing that is capable of determining beef quality is requiredThis research aims to develop an image segmentation method byprocessing digital images The system designed consists of image acquisition processes with varied distance resolution and angleImage segmentation is done to separate the images of fat and meat using the Otsu thresholding method Classification was carriedout using the decision tree algorithm and the best accuracies were obtained at 90 for training and 84 for testing Once developedthis system is then embedded into the android programming Results show that the image processing technique is capable of propermarbling score identification

1 Introduction

Beef is one of the many produce prone to contamination bymicroorganismWater andnutritional contentsmake an idealmedium for the growth and proliferation of microorganism[1 2] Contaminated beef will easily degrade and has lessstorage duration Beef class is valued by two factors itsprice and its quality The quality itself is measured by fourcharacteristics marbling meat color fat color and meatdensity Specifically marbling is the dominant parameter thatdetermines meatrsquos quality [3 4] Determination of beef qual-ity is commonly conducted visually by comparing the actualbeef and reference pictures of each beef class This processpresents weaknesses as it is subjective in nature and takes aconsiderable amount of timeTherefore an automated systembased on image processing that is capable of determiningbeef quality is required Some researches suggest that imageprocessing can be applied to analyze beef color and texturethat will in turn allow analysis results to be used as a referenceparameter in the process of meat quality identification [3ndash6]

Furthermore marbling grade evaluation has been conductedusing the watershed algorithm and artificial neural network[7]

This research aims to develop an image segmentationusing the Otsu thresholding method to separate the imagesof fat and meat Researches on image processing usingthresholding segmentation have been conducted before [8ndash12] The developed algorithm is proven capable of meatquality identification based on color and texture This systemis then embedded into the android programming to enableeven faster and easier use

2 Theory

Some researches on the application of image processing forbeef quality identification have been conducted earlier [4ndash6]One of those researches tried to determine the quality of beefusing texture analysis with the gray level cooccurrencematrix(GLCM)method [3] Beef quality is categorized into 12 gradesbased on the amount of fat it contains A research by Shiranita

HindawiJournal of Food QualityVolume 2017 Article ID 1674718 10 pageshttpsdoiorg10115520171674718

2 Journal of Food Quality

Figure 1 Beef quality levels based on marbling scores [3]

et al shows that theGLCMmethod is effective in determiningbeef quality Another research on the application of imagetexture to classify beef type yielded a correlation up to 08 [5]The other research that designed the hardware and softwarefor beef image segmentation using the vision thresholdmethod can be used as the initial process for beef qualitytesting [6] Those researches indicate that image processingbased on meat texture can be applied to identify beef qualitySome other researches also prove that mobile image analysismethods based on android programming are also applicable[13ndash15] Among these android related researches is imagerecognition using android smartphones [13] basic digitalimage processing using android [14] and application ofpower consumption meter based on image processing usingandroid smartphones [15] Results of those three researchesshow that image processing can be embedded into android-based mobile devices

Beef quality is categorized into 12 grades [3] as depictedin Figure 1 This is the result of image texture analysis usingthe gray level cooccurrence matrix (GLCM) method andmeat type recognition process using themulti-support vectormachine (MSVM) method

21 Image Segmentation and Feature Extraction The imageprocessing starts from image segmentation consisting of twostages The first is separating the meat and the backgroundThis process begins with thresholding the blue canal ofthe RGB (Red Green and Blue) image using the Otsuthresholding method to obtain a binary image Afterwardsthe binary image is used asmasking for object croppingOncethe object is separated from the background the second stageof segmentation that is meat and fat separation proceedsThis process itself starts by converting the RGB color spaceinto the grayscale color Then the process of thresholding toseparate meat and fat can be done Otsursquos method is also oneof the oldest methods of image segmentation that is treated

on statistical method according to the probabilistic imple-mentation [16] Otsursquos method is one of the best automaticthresholding methods [17] Basic principle of Otsursquos methodis to divide image into two classes that form object andbackground Automatic threshold is obtained by finding themaximum variance between two classes [9 10] If the [1 119871]is the grayscale in the image and 119875119894 is the probability of eachlevel the number of pixels with gray level 119894 is symbolized by119891119894 thus the probability of gray level 119894 in the image of equationsis given [16 18]

119875119894 = 119891119894119873 (1)

If 120583119879 is the automatic thresholding that divides the class intotwo classes 1198621 = [1 119905] and 1198622 = [119905 + 1 119871] [17 19]therefore the probability distribution of the degree of gray forthe two classes is

1198621 997888rarr [ 1198751sum119905119894=1 119875119894 1198752sum119905119894=1 119875119894

119875119905sum119905119894=1 119875119894]

1198622 997888rarr [ 119875119905+1sum119871119894=119905+1 119875119894 119875119905+2sum119871119894=119905+1 119875119894

119875119871sum119905119894=119905+1 119875119894] (2)

So the average range for classes 1198621 and 1198622 is1205831 = sum

119905119894=1 119894119875119894sum119905119894=1 119875119894

1205832 = sum119871119894=119905+1 119894119875119894sum119871119894=119905+1 119875119894

(3)

If 120583119879 is an overall average of the image therefore by addingup all the parts it became

12057311205831 + 12057321205832 = 120583119879 (4)

Journal of Food Quality 3

whereas

1205731 = 119905sum119894=1

119875119894

1205732 = 119871sum119894=119905+1

119875119894(5)

Total probability will always be the same with one so

1205731 + 1205732 = 1 (6)

ThereforeOtsuwill define variant between two classes1198621 and1198622 by the equation1205902 = 1205731 (1205831 minus 120583119879)2 + 1205732 (1205832 minus 120583119879)2 (7)

Optimal value threshold 119879 is the maximum value betweenvariant classes 1205902 that is shown by the following equation

119879 = max 1205902 (119905) 1 le 119905 le 119871 (8)

Once segmentation is done features extraction is carriedout based on two parameters of meat area and fat areaMeat area is the number of pixels it is made of whereasfat area is the number of pixels making up the fat areaBoth parameters were used as inputs for the classificationalgorithm that determines the marbling score

Otsursquos method is one of the global methods looking forthresholds that minimize class variance from original imagehistogram that is background and foreground The purposeof the Otsu thresholding method is to divide the gray levelimage histogram into two different regions automaticallywithout requiring the userrsquos help to enter the thresholdvalue The approach taken by Otsursquos method is to conducta discriminant analysis of determining a variable that candistinguish between two or more groups that arise naturallyDiscriminant analysis maximizes these variables in order tosplit the foreground and background objects Since the beefimage samples have a large variance between backgroundand object the Otsu thresholding method is appropriate fora meat quality identification system compared to traditionalsegmentation

22 Classification The classification algorithm used in thisresearch is decision tree algorithm using the C45 modelClassification is started by forming a root node followed byentropy value calculation for all data trained in the nodeParameters with maximum gain information were used asbreaking nodes that make branches Next if each node hasnot yielded one class label then entropy value calculationis repeated However when each node has yielded oneclass label then each of these nodes will be used as theleave nodes containing decisions [20ndash22] Based on research[21] the decision tree based C45 algorithm achieved thehighest classification accuracy comparedwith SupportVectorMachines (SVM) and Multilayer Perceptron (MLP) modelTherefore in this research we used the C45 model for theclassification of beef quality

Image acquisition

Image segmentation

(Otsuthresholding)

Featureextraction

(color analysis)Decision treeclassication

Figure 2 Research diagram block

3 Method

The system designed in this research includes processes ofimage acquisition image segmentation features extractionmarbling score classification and system embedding into theandroid programming The diagram block for this systemdesign is given in Figure 2

Algorithm 1 The following is an automatic threshold usingOtsursquos method and classification using decision tree as fol-lows

(1) Start(2) Load Image(3) Calculate the probability of each level of intensity(4) Set the initial values 120573119894 and 120583119894(5) Calculate the optimal threshold valueT with different

values 1 le 119905 le 119871(6) Update the value of 120573119894 and 120583119894(7) Calculate the value of 1205902(119905)(8) Desired threshold is the maximum value of 1205902(119905)(9) Calculate meat area (119860119898) and fat area (119860119891)(10) Input node parameters 119860119898 and 119860119891(11) Calculate entropy for all parameters in the node(12) Choose parameters with maximum gain value(13) Use those parameter as breaking node that make

braches(14) Each node only give one class label if true to step (13)

else step (9)(15) Node become leaves containing marbling score deci-

sions(16) End

4 Result and Discussion

The image processing stages involved in this research con-sist of image acquisition image segmentation and systemembedding into the android programming

41 Image Acquisition Results of beef image acquisitionalong with the marbling scores are given in Figure 3 Themarbling scores (MB) in this research are 4 5 6 7 and 9

Image acquisition is conducted vertically by varying thecamera distance resolution and angle The varied distancesare 20 cm and 30 cm In addition the varied resolutions are32MP 4MP and 5MP Samples of beef image resulting fromdistance and resolution variations are given in Table 1

In order to figure out the effect of angle in imageacquisition the following variations are made 0∘ 45∘ 90∘135∘ 180∘ 225∘ 270∘ 315∘ and 360∘ as depicted in Figure 4


Figure 3 Samples of acquired beef images

Table 1 Samples of beef image from varied camera distance and resolution

Resolution32MP 4MP 5MP

Distance

20 cm

30 cm

0 45 90

135 180 225

270 315 360

Figure 4 Samples of beef image with variation of camera angle


(a) RGB image (b) Blue canal

(c) Binary image (d) Segmentation result

Figure 5 The first stage of beef segmentation

(a) Grayscale image (b) Beef binary image

(c) Fat binary image (d) Meat binary image

Figure 6 The second stage of beef segmentation

Each image from every varied combination is taken twiceHence as many as 540 images were obtained

42 Image Segmentation The process of image segmentationto separate meat and fat consists of two stages First separat-ing the meat from the background This process begins withextracting the blue canal of the RGB image This extractedblue canal then undergoes the Otsu thresholding method toyield a binary imageThen this binary image is used as amaskin the process of meat cropping as shown in Figure 5

Once the meat is separated from the background Thenext step of image segmentation that separates the meat and

fat begins This process itself starts by converting the RGBcolor space into the grayscale color Then the process ofthresholding to separate meat and fat ensues The threshold-ing values set are 76 for fat and 30 for meatThis second stageof image segmentation is given in Figure 6

Those results show that all acquired images can beproperly segmented using the Otsu thresholding method

43 Features Extraction Features extraction is carried outbased on the parameters of meat area and fat area Meatarea is the number of pixels that make up that meat area(Figure 6(d)) while fat area is the number of pixels from


Table 2 Samples of features extraction

Number of image Image MB FeaturesMeat area Fat area

4 27859 529101

5 16355 487382

6 89665 99253

7 16873 602554

9 42868 455835

which it is made of (Figure 6(c)) The samples of featuresextraction results are shown in Table 2

Both categories of extracted features are then used asinputs in the process of beef quality classification

44 Classification In this research marbling score classifi-cation is carried out using the decision tree algorithm Thedecision tree algorithm for identification of beef quality isshown in Figure 7

The confusionmatrix that resulted from that decision treein the training process is given in Table 3

It can be seen in Table 3 that there are nine pieces of beefdata that arewrongly identifiedHence the resulting accuracyis

Accuracy = the number of correctly identified datatotal data

times 100= 8190 times 100 = 90

(9)

The confusion matrix that resulted from the decision treeis given in Table 4 This matrix is from the testing process

Fat area

Fat area MB 6

Meat area Meat area

MB 5 MB 7 MB 9 MB 4

lt429395 ge429395

lt21856 ge21856

lt867855 ge867855lt78627 ge78627

Figure 7 Decision tree for beef image with 4MP resolutions

It can be seen in Table 4 that there are 14 pieces of wronglyidentified beef data Therefore the resulting accuracy is 84


Table 3 Confusion matrix from decision tree in the training process

Predicted classMB 4 MB 5 MB 6 MB 7 MB 9

Actual class

MB 4 12 0 0 2 4MB 5 1 17 0 0 0MB 6 0 0 17 0 0MB 7 0 1 1 17 0MB 9 0 0 0 0 18

Table 4 Confusion matrix from decision tree in the testing process


Actual class

MB 4 12 1 0 2 3MB 5 1 14 0 0 0MB 6 0 0 17 0 0MB 7 1 2 1 15 0MB 9 3 0 0 0 18

Table 5 Overall results from both training and testing processes

Distance variation Resolution variation No variation20 cm 30 cm 32MP 4MP 5MP

Number of image Training 135 135 90 90 90 270Testing 135 135 90 90 90 270

Accuracy () Training 82 86 83 90 90 377Testing 80 81 81 84 78 3518

Results from both system training and testing using decisiontree algorithm are given in Table 5

Distance and resolution variations need to be doneto determine the best distance and minimum resolutionrequired by the system to properly acquire the beef imageVariation of distance will give an impact on the image detailobtained by smartphone camera so that the right distancewill get a good image Results from both system trainingand testing show that image acquisition at 30 cm gives betteraccuracy compared to acquisition from a 20 cm distanceOther than that image acquisition using a 4MP resolutioncamera yields better results compared to using both 32MPand 5MP resolution camerasWhile the variation of angle forbeef acquisition using smartphone camera has no significanteffect the process of testing data with various angle willbe recognized as beef with the same quality So it can beconcluded that the acquisition image with variation of angledoes not affect the beef quality identification process it can beseen in Figure 8 So the acquisition image can be taken fromvarious angles with the perpendicular position between thebeef and the camera smartphone

45 Android Smartphone Implementation This research usesboth hardware and software The hardware utilized is a tabletwith specifications given in Table 6

Meanwhile the software used is android studio andopenCV The process of system embedding into the androidprogramming is shown in Figure 9

Table 6 Hardware specification

BodyDimension 214 times 120 times 89mmWeight 328 g

DisplayType IPS LCD capacitive touchscreen 16M colorsSize 80 inches (sim723 screen-to-body ratio)Resolution 800 times 1280 pixels (sim189 ppi pixel density)

PlatformOS Android OS v442 (KitKat)Chipset Intel Atom Z3530CPU Quad-core 133GHzGPU PowerVR G6430

CameraPrimary 5MP autofocusFeatures GeotaggingVideo 720 pSecondary 2MP

The identification of marbling score developed in thisresearch has been properly embedded into the androidprogramming We have tested the beef quality identificationtime as shown in Table 7 From the test results the averageidentification time process was 284 s Based on these resultsthe system can identify the quality of beef quickly Therefore


Beef image

MeatGrayscale

MB 6

(a) Angle 45∘

Beef image

MeatGrayscale

MB 6

(b) Angle 135∘

Beef image

MeatGrayscale

MB 6

(c) Angle 180∘

Beef image

MeatGrayscale

MB 6

(d) Angle 315∘

Figure 8 Example of testing result for variation of angle with marbling score (MB) 6

Figure 9 System embedding into android

this method can be further used for future research such as inbeef quality identification based on video processing systems

5 Conclusion

Results show that the system developed in this researchis capable of acquiring and segmenting beef images and

identifying marbling score The variations involved in theprocess of image acquisition include camera resolutiondistance and angle The Otsu thresholding method is able toproperly separate images of fat and meat Classification wascarried out using the decision tree The resulting accuraciesare 90 for the training process and 84 for the testingprocess From the test results the average identification time


Table 7 Result of testing identification time

Sample Image Time (s)

Sample 1

Beef image

MeatGrayscale

MB 6

327

Sample 2

Beef image

MeatGrayscale

MB 7

262

Sample 3

Beef image

MeatGrayscale

MB 7

255

Sample 4

Beef image

MeatGrayscale

MB 9

248

Sample 5

Beef image

MeatGrayscale

MB 9

327

Average identification time 284

process was 284 s This system is then embedded into theandroid programming as to allow further research on beefquality identification based on video processing systems

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This research was funded by a program from the IndonesianDirectorate General of Higher Education in 2016

References

[1] J Li J Tan F A Martz and H Heymann ldquoImage texturefeatures as indicators of beef tendernessrdquoMeat Science vol 53no 1 pp 17ndash22 1999

[2] F Ruiz De Huidobro E Miguel B Blazquez and E OnegaldquoA comparison between two methods (Warner-Bratzler andtexture profile analysis) for testing either raw meat or cookedmeatrdquoMeat Science vol 69 no 3 pp 527ndash536 2005

[3] K Shiranita T Miyajima and R Takiyama ldquoDetermination ofmeat quality by texture analysisrdquo Pattern Recognition Lettersvol 19 no 14 pp 1319ndash1324 1998

[4] K Shiranita K Hayashi A Otsubo T Miyajima and RTakiyama ldquoGrading meat quality by image processingrdquo PatternRecognition vol 33 no 1 pp 97ndash104 2000

[5] O Basset B Buquet S Abouelkaram P Delachartre and JCulioli ldquoApplication of texture image analysis for the classifica-tion of bovinemeatrdquo Food Chemistry vol 69 no 4 pp 437ndash4452000

[6] K Chen and C Qin ldquoSegmentation of beef marbling based onvision thresholdrdquo Computers and Electronics in Agriculture vol62 no 2 pp 223ndash230 2008

[7] XMeng Y Sun Y Ni and Y Ren ldquoEvaluation of beef marblinggrade based on advanced watershed algorithm and neuralnetworkrdquo Advance Journal of Food Science and Technology vol6 no 2 pp 206ndash211 2014

[8] K Adi S Pujiyanto O D Nurhayati and A PamungkasldquoBeef quality identification using color analysis and k-nearestneighbor classificationrdquo in Proceedings of the 4th InternationalConference on Instrumentation Communications InformationTechnology and Biomedical Engineering ICICI-BME 2015 pp180ndash184 idn November 2015

[9] K Adi R Gernowo A Sugiharto K S Firdausi A Pamungkasand A B Putranto ldquoTuberculosis (TB) Identification in TheZiehl-Neelsen Sputum Sample in NTSC Channel and SupportVector Machine (SVM) Classificationrdquo International Journal ofInnovative Research in Science Engineering and Technology vol2 no 9 pp 5030ndash5035 2013

[10] K Adi R Gernowo A Sugiharto A Pamungkas A BPutranto and N Mirnasari ldquoAutothresholding SegmentationFor Tuberculosis Bacteria Identification In The Ziehl-NeelsenSputum Samplerdquo in Proceedings of the 7th International Confer-ence on Information amp Communication Technology and SystemsBali Indonesia 2013

[11] K Adi S Pujiyanto R Gernowo A Pamungkas and A BPutranto ldquoIdentification of plasmodium falciparum phase inred blood cells using artificial neural networksrdquo InternationalJournal of Applied Engineering Research vol 9 no 22 pp 13917ndash13924 2014

[12] A Pamungkas K Adi and R Gernowo ldquoIdentification ofplasmodium falciparum development phase inmalaria infectedred blood cells using adaptive color segmentation and decisiontree based classificationrdquo International Journal of Applied Engi-neering Research vol 10 no 2 pp 4043ndash4056 2015

[13] I Billiauws and K Bonjean Image Recognition on an AndroidMobile Phone (Msc thesis) Elektronica-Ict 2009

[14] R S Jagtap and J A Sadalage ldquoImage Processing as AndroidApplicationrdquo International Journal of Science and ResearchTechnology vol 1 no 2 pp 12ndash15 2013

[15] K M Saipullah A Anuar N A Ismail and Y Soo ldquoMeasuringpower consumption for image processing on Android smart-phonerdquo American Journal of Applied Sciences vol 9 no 12 pp2052ndash2057 2012

[16] N Otsu ldquoA threshold selection method from gray-level his-togramrdquo IEEE Transactions on System Man Cybernetics vol 9no 1 pp 62ndash66 1979

[17] D-Y Huang T-W Lin and W-C Hu ldquoAutomatic multilevelthresholding based on two-stage Otsursquos method with clusterdetermination by valley estimationrdquo International Journal ofInnovative Computing Information and Control vol 7 no 10pp 5631ndash5644 2011

[18] F Jassim and F H AAltaani ldquoHybridization of otsu methodand median filter for color image segmentationrdquo InternationalJournal of Soft Computing and Engineering (IJSCE) vol 3 no 22013

[19] H K Singh S K Tomar and P K Maurya ldquoThresholdingTechniques applied for Segmentation of RGB andmultispectral


imagesrdquo in Proceedings of the MPGI National Multi Conference2012 (MPGINMC-2012 Advancement in Electronics Telecommu-nication Engineering) 2012

[20] H Chauhan and A Chauhan ldquoImplementation of decision treealgorithm c45rdquo International Journal of Scientific and ResearchPublications vol 3 no 10 2013

[21] F S Alaa and A Amneh ldquoA Professional Comparison of C45MLP SVM for Network Intrusion Detection based FeatureAnalysisrdquo ICGST Journal of Computer Networks and InternetResearch vol 6 no 3 pp 192ndash205 November 2015

[22] J Jashan and M Bag ldquoCascading of C45 Decision Tree andSupport Vector Machine for Rule Based Intrusion DetectionSystemrdquo International Journal of Computer Network and Infor-mation Security vol 4 no 8 pp 8ndash20 2012

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 201


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


Figure 1 Beef quality levels based on marbling scores [3]

et al shows that theGLCMmethod is effective in determiningbeef quality Another research on the application of imagetexture to classify beef type yielded a correlation up to 08 [5]The other research that designed the hardware and softwarefor beef image segmentation using the vision thresholdmethod can be used as the initial process for beef qualitytesting [6] Those researches indicate that image processingbased on meat texture can be applied to identify beef qualitySome other researches also prove that mobile image analysismethods based on android programming are also applicable[13ndash15] Among these android related researches is imagerecognition using android smartphones [13] basic digitalimage processing using android [14] and application ofpower consumption meter based on image processing usingandroid smartphones [15] Results of those three researchesshow that image processing can be embedded into android-based mobile devices

Beef quality is categorized into 12 grades [3] as depictedin Figure 1 This is the result of image texture analysis usingthe gray level cooccurrence matrix (GLCM) method andmeat type recognition process using themulti-support vectormachine (MSVM) method

21 Image Segmentation and Feature Extraction The imageprocessing starts from image segmentation consisting of twostages The first is separating the meat and the backgroundThis process begins with thresholding the blue canal ofthe RGB (Red Green and Blue) image using the Otsuthresholding method to obtain a binary image Afterwardsthe binary image is used asmasking for object croppingOncethe object is separated from the background the second stageof segmentation that is meat and fat separation proceedsThis process itself starts by converting the RGB color spaceinto the grayscale color Then the process of thresholding toseparate meat and fat can be done Otsursquos method is also oneof the oldest methods of image segmentation that is treated

on statistical method according to the probabilistic imple-mentation [16] Otsursquos method is one of the best automaticthresholding methods [17] Basic principle of Otsursquos methodis to divide image into two classes that form object andbackground Automatic threshold is obtained by finding themaximum variance between two classes [9 10] If the [1 119871]is the grayscale in the image and 119875119894 is the probability of eachlevel the number of pixels with gray level 119894 is symbolized by119891119894 thus the probability of gray level 119894 in the image of equationsis given [16 18]

119875119894 = 119891119894119873 (1)

If 120583119879 is the automatic thresholding that divides the class intotwo classes 1198621 = [1 119905] and 1198622 = [119905 + 1 119871] [17 19]therefore the probability distribution of the degree of gray forthe two classes is

1198621 997888rarr [ 1198751sum119905119894=1 119875119894 1198752sum119905119894=1 119875119894

119875119905sum119905119894=1 119875119894]

1198622 997888rarr [ 119875119905+1sum119871119894=119905+1 119875119894 119875119905+2sum119871119894=119905+1 119875119894

119875119871sum119905119894=119905+1 119875119894] (2)

So the average range for classes 1198621 and 1198622 is1205831 = sum

119905119894=1 119894119875119894sum119905119894=1 119875119894

1205832 = sum119871119894=119905+1 119894119875119894sum119871119894=119905+1 119875119894

(3)

If 120583119879 is an overall average of the image therefore by addingup all the parts it became

12057311205831 + 12057321205832 = 120583119879 (4)


whereas

1205731 = 119905sum119894=1

119875119894

1205732 = 119871sum119894=119905+1

119875119894(5)


1205731 + 1205732 = 1 (6)



119879 = max 1205902 (119905) 1 le 119905 le 119871 (8)




Image acquisition

Image segmentation

(Otsuthresholding)

Featureextraction



3 Method







sions(16) End










Distance

20 cm

30 cm

0 45 90

135 180 225

270 315 360


















4 27859 529101

5 16355 487382

6 89665 99253

7 16873 602554

9 42868 455835







times 100= 8190 times 100 = 90

(9)


Fat area

Fat area MB 6

Meat area Meat area

MB 5 MB 7 MB 9 MB 4

lt429395 ge429395

lt21856 ge21856

lt867855 ge867855lt78627 ge78627






Actual class

MB 4 12 0 0 2 4MB 5 1 17 0 0 0MB 6 0 0 17 0 0MB 7 0 1 1 17 0MB 9 0 0 0 0 18



Actual class

MB 4 12 1 0 2 3MB 5 1 14 0 0 0MB 6 0 0 17 0 0MB 7 1 2 1 15 0MB 9 3 0 0 0 18
















Beef image

MeatGrayscale

MB 6

(a) Angle 45∘

Beef image

MeatGrayscale

MB 6

(b) Angle 135∘

Beef image

MeatGrayscale

MB 6

(c) Angle 180∘

Beef image

MeatGrayscale

MB 6

(d) Angle 315∘




5 Conclusion






Sample 1

Beef image

MeatGrayscale

MB 6

327

Sample 2

Beef image

MeatGrayscale

MB 7

262

Sample 3

Beef image

MeatGrayscale

MB 7

255

Sample 4

Beef image

MeatGrayscale

MB 9

248

Sample 5

Beef image

MeatGrayscale

MB 9

327





Acknowledgments


References
































Volume 201






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


whereas

1205731 = 119905sum119894=1

119875119894

1205732 = 119871sum119894=119905+1

119875119894(5)


1205731 + 1205732 = 1 (6)



119879 = max 1205902 (119905) 1 le 119905 le 119871 (8)




Image acquisition

Image segmentation

(Otsuthresholding)

Featureextraction



3 Method







sions(16) End










Distance

20 cm

30 cm

0 45 90

135 180 225

270 315 360


















4 27859 529101

5 16355 487382

6 89665 99253

7 16873 602554

9 42868 455835







times 100= 8190 times 100 = 90

(9)


Fat area

Fat area MB 6

Meat area Meat area

MB 5 MB 7 MB 9 MB 4

lt429395 ge429395

lt21856 ge21856

lt867855 ge867855lt78627 ge78627






Actual class

MB 4 12 0 0 2 4MB 5 1 17 0 0 0MB 6 0 0 17 0 0MB 7 0 1 1 17 0MB 9 0 0 0 0 18



Actual class

MB 4 12 1 0 2 3MB 5 1 14 0 0 0MB 6 0 0 17 0 0MB 7 1 2 1 15 0MB 9 3 0 0 0 18
















Beef image

MeatGrayscale

MB 6

(a) Angle 45∘

Beef image

MeatGrayscale

MB 6

(b) Angle 135∘

Beef image

MeatGrayscale

MB 6

(c) Angle 180∘

Beef image

MeatGrayscale

MB 6

(d) Angle 315∘




5 Conclusion






Sample 1

Beef image

MeatGrayscale

MB 6

327

Sample 2

Beef image

MeatGrayscale

MB 7

262

Sample 3

Beef image

MeatGrayscale

MB 7

255

Sample 4

Beef image

MeatGrayscale

MB 9

248

Sample 5

Beef image

MeatGrayscale

MB 9

327





Acknowledgments


References
































Volume 201






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





Distance

20 cm

30 cm

0 45 90

135 180 225

270 315 360


















4 27859 529101

5 16355 487382

6 89665 99253

7 16873 602554

9 42868 455835







times 100= 8190 times 100 = 90

(9)


Fat area

Fat area MB 6

Meat area Meat area

MB 5 MB 7 MB 9 MB 4

lt429395 ge429395

lt21856 ge21856

lt867855 ge867855lt78627 ge78627






Actual class

MB 4 12 0 0 2 4MB 5 1 17 0 0 0MB 6 0 0 17 0 0MB 7 0 1 1 17 0MB 9 0 0 0 0 18



Actual class

MB 4 12 1 0 2 3MB 5 1 14 0 0 0MB 6 0 0 17 0 0MB 7 1 2 1 15 0MB 9 3 0 0 0 18
















Beef image

MeatGrayscale

MB 6

(a) Angle 45∘

Beef image

MeatGrayscale

MB 6

(b) Angle 135∘

Beef image

MeatGrayscale

MB 6

(c) Angle 180∘

Beef image

MeatGrayscale

MB 6

(d) Angle 315∘




5 Conclusion






Sample 1

Beef image

MeatGrayscale

MB 6

327

Sample 2

Beef image

MeatGrayscale

MB 7

262

Sample 3

Beef image

MeatGrayscale

MB 7

255

Sample 4

Beef image

MeatGrayscale

MB 9

248

Sample 5

Beef image

MeatGrayscale

MB 9

327





Acknowledgments


References
































Volume 201






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

















4 27859 529101

5 16355 487382

6 89665 99253

7 16873 602554

9 42868 455835







times 100= 8190 times 100 = 90

(9)


Fat area

Fat area MB 6

Meat area Meat area

MB 5 MB 7 MB 9 MB 4

lt429395 ge429395

lt21856 ge21856

lt867855 ge867855lt78627 ge78627






Actual class

MB 4 12 0 0 2 4MB 5 1 17 0 0 0MB 6 0 0 17 0 0MB 7 0 1 1 17 0MB 9 0 0 0 0 18



Actual class

MB 4 12 1 0 2 3MB 5 1 14 0 0 0MB 6 0 0 17 0 0MB 7 1 2 1 15 0MB 9 3 0 0 0 18
















Beef image

MeatGrayscale

MB 6

(a) Angle 45∘

Beef image

MeatGrayscale

MB 6

(b) Angle 135∘

Beef image

MeatGrayscale

MB 6

(c) Angle 180∘

Beef image

MeatGrayscale

MB 6

(d) Angle 315∘




5 Conclusion






Sample 1

Beef image

MeatGrayscale

MB 6

327

Sample 2

Beef image

MeatGrayscale

MB 7

262

Sample 3

Beef image

MeatGrayscale

MB 7

255

Sample 4

Beef image

MeatGrayscale

MB 9

248

Sample 5

Beef image

MeatGrayscale

MB 9

327





Acknowledgments


References
































Volume 201






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Actual class

MB 4 12 0 0 2 4MB 5 1 17 0 0 0MB 6 0 0 17 0 0MB 7 0 1 1 17 0MB 9 0 0 0 0 18



Actual class

MB 4 12 1 0 2 3MB 5 1 14 0 0 0MB 6 0 0 17 0 0MB 7 1 2 1 15 0MB 9 3 0 0 0 18
















Beef image

MeatGrayscale

MB 6

(a) Angle 45∘

Beef image

MeatGrayscale

MB 6

(b) Angle 135∘

Beef image

MeatGrayscale

MB 6

(c) Angle 180∘

Beef image

MeatGrayscale

MB 6

(d) Angle 315∘




5 Conclusion






Sample 1

Beef image

MeatGrayscale

MB 6

327

Sample 2

Beef image

MeatGrayscale

MB 7

262

Sample 3

Beef image

MeatGrayscale

MB 7

255

Sample 4

Beef image

MeatGrayscale

MB 9

248

Sample 5

Beef image

MeatGrayscale

MB 9

327





Acknowledgments


References
































Volume 201






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Beef image

MeatGrayscale

MB 6

(a) Angle 45∘

Beef image

MeatGrayscale

MB 6

(b) Angle 135∘

Beef image

MeatGrayscale

MB 6

(c) Angle 180∘

Beef image

MeatGrayscale

MB 6

(d) Angle 315∘




5 Conclusion






Sample 1

Beef image

MeatGrayscale

MB 6

327

Sample 2

Beef image

MeatGrayscale

MB 7

262

Sample 3

Beef image

MeatGrayscale

MB 7

255

Sample 4

Beef image

MeatGrayscale

MB 9

248

Sample 5

Beef image

MeatGrayscale

MB 9

327





Acknowledgments


References
































Volume 201






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Sample 1

Beef image

MeatGrayscale

MB 6

327

Sample 2

Beef image

MeatGrayscale

MB 7

262

Sample 3

Beef image

MeatGrayscale

MB 7

255

Sample 4

Beef image

MeatGrayscale

MB 9

248

Sample 5

Beef image

MeatGrayscale

MB 9

327





Acknowledgments


References
































Volume 201






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology













Volume 201






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

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