Jestr Journal of Engneerng Scence and Technology Revew 11 (2) (2018) 8-12 Research Artcle Detecton Lung Cancer Usng Gray Level Co-Occurrence Matrx (GLCM) and Back Propagaton Neural Network Classfcaton Kusworo Ad 1, Catur Ed Wdodo 1, Ars Pu Wdodo 2, Rahmat Gernowo 1, Ad Pamungkas 3 and Rzky Ayom Syfa 1 1 Department of Physcs, Faculty of Scence and Mathematcs, Dponegoro Unversty 2 Department of Informatcs, Faculty of Scence and Mathematcs, Dponegoro Unversty 3 Electromedcal Engneerng Academy, Indonesa JOURNAL OF Engneerng Scence and Technology Revew www.estr.org Receved 8 October 2017; Accepted 7 March 2018 Abstract Lung cancer prevalence s one of the hghest of cancers, at 18 %. One of the frst steps n lung cancer dagnoss s samplng of lung tssues or bopsy. These tssue samples are then mcroscopcally analyzed. Ths procedure s taken once magng tests ndcate the presence of cancer cells n the chest. Lung cancer dagnoss usng lung tssue sample mcroscopc analyss has some weakness. One of them s that doctor stll reles on subectve vsual observaton. A medcal specalst must do thorough observaton and accurate analyss n detectng lung cancer n patents. Hence, there s need for a system that s capable for detectng lung cancer automatcally from mcroscopc mages of bopsy. Ths method wll mprove the accuracy and effcency for lung cancer detecton. The am of ths research s to desgn a lung cancer detecton system based on analyss of mcroscopc mage of bopsy usng dgtal mage processng. Mcroscopc mages of bopsy are feature extracted wth the Gray Level Co-Occurrence Matrx (GLCM) method and classfed usng back propagaton neural network. Ths method s mplemented to detecton both normal and cancerous lung of bopsy samples. In the stage of tranng, 20 bopsy mage samples were analyzed usng back propagaton neural network wth 95% accuracy. On the other hand, 16 bopsy samples were analyzed durng testng, wth an accuracy of 81.25%. These results show that mcroscopc bopsy mage processng can be mplemented n a system of lung cancer detecton. Keywords: lung cancer, mage processng, bopsy, mcroscopc, back propagaton neural network 1. Introducton Prevalence of lung cancer s the hghest of all types of cancer, whch s 18% [1]. Lung cancer examnaton s done n three stages, that are CT Scan analyss, sputum examnaton, and lung tssue samplng (bopsy). The frst step s usually an X-ray magng, ths step reveals the presence of lung cancer. The next two steps are needed for confrmaton, that s sputum examnaton to fnd out there are cancer cells n the lungs and the bopsy examnaton s ntended to show the presence of cancer cells n the chest. Dagnoss of lung cancer by mcroscopc analyss of lung tssue has some dsadvantages wth vsual subectve. Therefore, a system that s able to automatcally overcome lung cancer n the mcroscopc bopsy mage to mprove the obectvty and effcency of lung cancer detecton. Dgtal mage processng technques are able to overcome lung cancer wth varous methods offered. Ths technque has been appled to varousmedcal applcatons such as the detecton of tuberculoss bactera n mcroscopc sputum mages [2,3], malara detecton causng phase of plasmodum falcparum [4,5,6], detecton of lung cancer obects n CT scan [ 7, 8,9], and analyss of mcroscopc sputum samples for lung cancer [10,11,12]. Dagnoss of lung cancer wth Naïve Bayes classfcaton has been *E-mal address: kusworoad@fska.undp.ac.d ISSN: 1791-2377 2018 Eastern Macedona and Thrace Insttute of Technology. All rghts reserved. do:10.25103/estr.112.02 performed n prevous research [13]. In ths study was done by Gray Level Co-Occurrence Matrx (GLCM) method and the results 88.57%. Dgtal mage processng for tuberculoss bactera detecton on mcroscopc sputum mages (Zehl-Neelsen Sputum Sample) has been developed n one research usng the Otsu thresholdng segmentaton method on the HSV channel. The dentfcaton characterstcs for tuberculoss bactera are eccentrcty, compactness, and metrc, whereas the dentfcaton algorthm s back propagaton neural network. Ths research resulted n an accuracy coeffcent of 0.990 [3]. The other research to detect tuberculoss bactera was carred out usng the Otsu thresholdng segmentaton method on the NTSC channel. The dentfcaton characterstcs are eccentrcty and compactness, whle the dentfcaton algorthm s the Support Vector Machne (SVM). Ths research resulted n a hghly accurate detecton system for mcroscopc sputum mages to detect tuberculoss bactera [4]. Ths next research was focused on the detecton of malara causng plasmodum falcparum usng a mcroscopc magng technque. The detecton process used the Otsu thresholdng segmentaton method on the RGB color channel, and the dentfcaton algorthm used was back propagaton neural network wth plasmodum bnary characterstcs as ts nput. Ths partcular research yelded an accuracy of 87.5% n dentfyng the developmental phase of plasmodum falcparum [5]. Yet another research successfully developed a system that employs a thresholdng
Kusworo Ad, Catur Ed Wdodo, Ars Pu Wdodo, Rahmat Gernowo, Ad Pamungkas and Rzky Ayom Syfa/ segmentaton method on the HSV channel wth the decson tree algorthm used. Ths was amed at dentfyng the developmental phase of plasmodum falcparum. The characterstcs used for dentfcaton here are comparsons of mage area and eccentrcty. Ths research came up wth an accuracy of 87.67% [6]. More samples of research on dentfcaton of plasmodum falcparum developmental phase employed adaptve color segmentaton method and artfcal neural network classfcaton. Adaptve color segmentaton was carred out on the HSV channel and the classfcaton algorthm s back propagaton neural network. Ths system scored 87.80% and 87.14% n plasmodum falcparum developmental phase dentfcaton accuracy, durng tranng and testng, respectvely [7]. The other research on lung cancer detecton from CT scan mages was carred usng the medan flter to enhance mage qualtes. The segmentaton process was thresholdng, whle both morphology operaton and edge detecton were used to know the area of the lung. The dentfcaton process used here has four algorthms of Sequental Mnmal Optmzaton (SMO), J48 Decson Tree, Logt Boost, and Nave Bayes. The hghest accuracy was recorded for the Logt Boost segmentaton process, wth an accuracy of 98% [8]. Ths next research was focused on de-nosng and the Wener flter to get rd of nose on the mages Thresholdng and watershed methods were then employed. Meanwhle, the characterstcs used for dentfcaton are area, crcumference, and eccentrcty. On the other, the dentfcaton process made us of the Support Vector Machnes (SVM). It results n a hghly accurate lung cancer detecton system [9]. The next research developed a Non Local Mean flter to get rd of noses. Ths method made use of the Otsu thresholdng method. Structural and texture characterstcs were then used as nputs n dentfyng cancer cells wth the help of the Support Vector Machnes (SVM) algorthm. Ths resulted n 95.12% accuracy [10]. Other than beng appled on CT scan lung mages, dgtal mage processng has also been utlzed on mcroscopc sputum mages to detect cancer cells. A research on sputum mages was then further mproved wth the thresholdng method to separate cancer obects on mages of mcroscopc sputum. A hstogram analyses was then used as an nput to dentfy those cancer obects. The dentfcaton process employed the Bayesan classfcaton and mean shft algorthms that resulted n an 87% accuracy [11]. The next research developed the thresholdng method to separate nucle (Blue Dyes) from cytoplasm (Red Dyes). The dentfcaton method used for the cancer cells was gray pxel, RGB pxel, and HSV pxel classfcatons. Ths research came up wth hghly accurate results as well [12]. One further research developed the method of hstogram equalzaton to enhance mage qualty. The segmentaton process here also employed artfcal neural network algorthm. Ths was then compared to the thresholdng method. Results showed that artfcal neural network s better than the thresholdng method [14]. Based on the above mentoned ntroducton and the prevous researches, ths research desgns a system of lung cancer detecton based on analyss of mcroscopc lung bopsy mage. Ths system wll help mprovng lung cancer examnaton by provdng an automatc and obectve technque that wll certanly be benefcal n adng doctor n accurately dagnosng and treatng lung cancer. 2. Theory 2.1. Image Processng A dgtal mage s stated as a two dmensonal functon f(x, y), n whch x and y are pxel coordnate postons, whereas f s the ampltude at coordnate (x, y), whch ndcates a value of pxel ntensty. A dgtal mage can be stated as the followng matrx [14]: ( ) = f x, y f (0,0) f (0,1) f (0,n 1) f (1,0) f (1,1) f (1,n 1)!!!! f (m 1, 0) f (m 1,1) f (m 1,n 1) Where m s mage heght and n s mage wdth. Image processng s a dscplne concernng thngs related to mprovements of mage qualty, transformaton, and features. Image processng s amed at analyzng, extractng nformaton, gvng descrpton, or recognzng obects n an mage. Data compresson and reducton for the purpose of storage, transmsson, and processng are also some of the aspects studed n mage processng. The nputs themselves are mages, whle the outputs are processed mages. 2.2 Features Extracton Features extracton s the stage of hghlghtng and reducng an mage from ts hgher to lower dmenson. Features extracton s a quanttatve nformaton selecton from readly avalable characterstcs that classfy obect classes. Features extracton measures quanttatve characterstcs of each pxel. Obect recognton requres certan parameters that characterze that partcular obect. These parameters nclude shape, color, sze, and texture. Each obect s extracted for ts features based on certan parameters and s then assgned a certan class. The GLCM feature extracton method s a matrx that descrbes the occurrence frequency of two pxels wth certan ntenstes at dstance d and angular orentaton θ wthn an mage. GLCM feature extracton s carred out n 4 angular drecton, each of whch wth a 45 nterval; 0, 45, 90, 135. Features extracton that employs texture analyss s conducted by takng grayscale characterstcs of an obect that dfferentate t from the other obects. These extracted characterstcs nclude contrast, correlaton, energy, and homogenety [15][16][17]. 2.2.1.Contrast Contrast features are used to calculate the degree of dfference of grayness n an mage. The greater the dfference of grayness, the hgher the contrast s. On the contrary, the less sgnfcant the dfference of grayness between two pxels, the lower the contrast wll be. Contrast s defned as: Contrast = where p(, ) s the GLCM matrx (1) ( ) 2 p(, ) (2) 9
Kusworo Ad, Catur Ed Wdodo, Ars Pu Wdodo, Rahmat Gernowo, Ad Pamungkas and Rzky Ayom Syfa/ 2.2.2. Correlaton Correlaton brngs out how correlated a reference pxel to ts neghbor over an mage. Correlaton s defned as: Correlaton = P d (, ) µ x µ y (3) σ x σ y where µ x, µ y and σ x,σ y are the mean and standard devatons of probablty matrx GLCM along row wse x and column wse y. 2.2.3. Energy Energy value descrbes the degree of grayness dstrbuton n an mage. Energy s wrtten as: Energy = p2 (, ) (4) 4. Result and Dscusson 4.1. Mcroscope Lung Bopsy Image There are 38 mages of mcroscopc lung bopsy that are categorzed nto cancer and non-cancer categores. Twenty (20) of those mages belong to the cancer category and the remanng eghteen (18) s of non-cancer category. Some samples of mcroscopc lung bopsy mages are shown n Fg. 2. 2.2.4. Homogenety Homogenety features calculate the degree of homogenety of grayness n an mage. Homogenety value s hgher n mages of almost the same degree of grayness. Homogenety s defned as: p(, ) Homogenety = (5) 1+ 3. Method Ths research develops a system of lung cancer detecton based on the analyss of mcroscopc bopsy mages usng the technque of dgtal mage processng. The procedure for mage processng nclude convertng RGB mages nto grayscale, extractng texture characterstcs, and classfyng usng back propagaton neural network algorthm. A block dagram for lung cancer dagnoss system usng the technque of dgtal mage processng s gven n Fg. 1. Fg. 1. Block dagram of the mcroscopc bopsy mage analyss system. a. Read Image The mage of bopsy samples taken from http://www.eusatlas.ro, http://www.pathologyoutlnes.com,and http://www.sumed.edu/. b. RGB to grayscale mage converson Mcroscopc lung bopsy mages that comes n RGB format s converted nto grayscale usng the equaton: 0.2989 * R + 0.5870 * G + 0.1140 * B. c. Texture characterstcs extracton Converted grayscale mages are then analyzed for ther texture usng the Gray Level Co-occurrence Matrx method as to obtan texture parameters of contrast, correlaton, energy, and homogenety. d. Back Propagaton Neural Network Images are classfed nto two classes of cancer and noncancer usng the artfcal neural network algorthm. Fg. 2. Samples of mcroscopc lung bopsy mages. (Above: cancer; below: non-cancer). The process of mage features extracton s carred out wth texture analyss usng the Gray Level Co-Occurrence Matrx (GLCM) method. Ths method works on the prncple of calculatng the probablty of nearest neghbor between two pxels on certan dstance and angular orentaton. Ths approach bulds co-occurrence matrces of mage data, whch n turn determne features as the matrx functon of those mages. Co-occurrence means happenng at the same tme. Ths translates to the probablty of one level of a pxel value beng nearest to a value level of another pxel at certan dstance (d) and angular orentaton (θ). Dstance s stated as pxels, whle orentaton s n degrees. Orentaton s made up of four angular drectons, each wth a 45º nterval. They are; 0º, 45º, 90º, and 135º, whereas the dstance between two pxels s gven as1 pxel. A co-occurrence matrx s a square matrx whose number or elements s the square of pxel ntensty level on an mage. Each pont ( p,q) on a co-occurrence matrx contans the probablty of a pxel of value p beng nearest to a neghborng pxel of value q at dstance d and orentaton θ. Then from that co-occurrence matrx, parameters of contrast, correlaton, energy, and homogenety are extracted as texture features. Samples of extracted mcroscopc lung bopsy mage features are gven n Table 1. 4.2. Image Classfcaton The process of mcroscopc lung bopsy mage s carred out usng the back propagaton artfcal neural network algorthm. Ths process conssts of two stages..e. tranng and testng. The tranng stage used 20 mages of whch 10 were of cancer and the other 10 were of non-cancer. The four extracted features of contrast, correlaton, energy, and homogenety serve as nputs for the artfcal neural network algorthm, whereas the outputs are categores of cancer and non-cancer. The archtecture of back propagaton neural network conssts of three layers,.e. nput, hdden, and output. Parameters used for the neural network are gven n Table 2. 10
Kusworo Ad, Catur Ed Wdodo, Ars Pu Wdodo, Rahmat Gernowo, Ad Pamungkas and Rzky Ayom Syfa/ Table 1. Samples extracted features from mcroscopc lung bopsy mages. Results of mcroscopc lung bopsy mage classfcaton durng tranng usng the back propagaton neural network s shown n Table 3. Table 3. Tranng results for mcroscopc lung bopsy mage classfcaton usng back propagaton neural network. Table 2. Parameters used for back propagaton artfcal neural network.. It can be seen n Table 3 that one mage class were ncorrectly predcted that the accuracy of the tranng system s gven as: number of correctly predcted class accuracy = 100% number of total class = 19 20 100% = 95% The artfcal neural network resulted durng the tranng stage s then used to predct mage class on the testng stage. Results of artfcal neural network on the testng stage are gven n Table 4. Layout of the neural network archtecture s shown n Fg. 3. Table 4. Testng results for mcroscopc lung bopsy mage classfcaton usng back propagaton neural network. Fg. 3. Archtecture for the back propagaton neural network. 11
Kusworo Ad, Catur Ed Wdodo, Ars Pu Wdodo, Rahmat Gernowo, Ad Pamungkas and Rzky Ayom Syfa/ It s evdent n Table 4 that fve mage classes were ncorrectly predcted that the resultng testng system accuracy s gven as: accuracy = number of correctly predcted class 100% number of total class = 13 16 100% = 81.25% Results from both tranng and testng stages of the developed artfcal neural network algorthm show that ths algorthm s capable of properly classfyng mcroscopc lung bopsy mages nto ether cancer or non-cancer class. 5. Concluson Ths research has successfully developed a system of mcroscopc lung bopsy mage analyss to detect lung cancer. The dgtal mage processng nvolves texture References features extracton usng the Gray Level Co-Occurrence Matrx (GLCM) method and mage classfcaton usng the back propagaton neural network algorthm. Texture features are extracted based on parameters of contrast, correlaton, energy, and homogenety, whereas mcroscopc lung bopsy mages are classfed nto ether cancer or non-cancer class usng the artfcal neural network algorthm. Ths newly developed system s capable of classfyng mages wth 95% accuracy on the tranng stage, and 81.25% accuracy on the testng stage. These two results prove that ths system s sutable to be mplemented for lung cancer detecton purposes. Acknowledgment Ths research was funded by the Indonesan Drectorate General of Hgher Educaton Program n 2016 and 2017. The mages of bopsy were used n ths research s downloaded n http://www.eusatlas.ro, http://www.pathologyoutlnes.com, and http://www.sumed.edu/, the authors would lke to thank to supported mage sample of bopsy. Ths s an Open Access artcle dstrbuted under the terms of the Creatve Commons Attrbuton Lcense 1. World Health Organsaton, WHO report on the Global Tobacco Epdemc. 2008. 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