Research Article Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities

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1 Hndaw Publshng Corporaton Internatonal Journal of Bomedcal Imagng Volume 2015, Artcle ID , 7 pages Research Artcle Statstcal Analyss of Haralck Texture Features to Dscrmnate Lung Abnormaltes Nourhan Zayed and Heba A. Elnemr Computer & Systems Department, Electroncs Research Insttute, Caro 12611, Egypt Correspondence should be addressed to Nourhan Zayed; nourhan@er.sc.eg Receved 23 May 2015; Revsed 10 September 2015; Accepted 15 September 2015 Academc Edtor: Tange Zhuang Copyrght 2015 N. Zayed and H. A. Elnemr. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. The Haralck texture features are a well-known mathematcal method to detect the lung abnormaltes and gve the opportunty to the physcan to localze the abnormalty tssue type, ether lung tumor or pulmonary edema. In ths paper, statstcal evaluaton of the dfferent features wll represent the reported performance of the proposed method. Thrty-seven patents CT datasets wth ether lung tumor or pulmonary edema were ncluded n ths study. The CT mages are frst preprocessed for nose reducton and mage enhancement, followed by segmentaton technques to segment the lungs, and fnally Haralck texture features to detect the type of the abnormalty wthn the lungs. In spte of the presence of low contrast and hgh nose n mages, the proposed algorthms ntroduce promsng results n detectng the abnormalty of lungs n most of the patents n comparson wth the normal and suggest that some of the features are sgnfcantly recommended than others. 1. Introducton The lung s an organ that performs a multtude of vtal functons every second of our lves. Ths fact leads to consderng lung abnormaltes, lfe-sustaned dseases that have hgh prorty n detecton, dagnoss, and treatment f possble. Our focus n ths paper wll be on two popular abnormaltes wthn the lung, whch are pulmonary edema and lung tumor. Pulmonary edema (water n the lungs) s caused by flud buldng up n the ar sacs of the lungs [1, 2]. On the other hand, lung cancer/tumor s a dsease where uncontrolled cell growth n tssues of the lung occurred [3]. Computer-aded dagnoss (CAD) schemes for thoracc computed tomography (CT) are wdely used to characterze, quantfy, and detect numerous lung abnormaltes, such as pulmonary edema and lung cancer [4, 5]. An accurate lung segmentaton method s always a crtcal frst step n these CAD schemes and can sgnfcantly mprove the performance level of these schemes. Although manual or semautomatc lung segmentaton methods for CT mages were used n some early CAD schemes [6 10], they are mpractcal for current CAD schemes because multdetector CT (MDCT) scanners can generate hundreds of CT slces for a patent. An automated method for lung segmentaton s needed for MDCT. In addton, the eye dentfcaton/detecton of the abnormalty type (pulmonary edema or tumors) n computed tomography (CT) mages s very dffcult even for the experenced clncans because of ts varable shape along wth low contrast and hgh nose assocated wth t. As the fnal stage of treatng the lung cancer s surgcal removal of the dseased lung, hence t s necessary to dentfy the cancer locaton, whch can be useful before they plan for the surgery. The am of our work s to develop an automated novel textureanalyssbasedmethodforthesegmentatonofthelungs and the detecton of the abnormaltes, whether pulmonary edema or lung tumor. Haralck s features based on the gray level cooccurrence matrx (GLCM) are appled to capture textural patterns n lung mages. The obectve of ths work s the selecton of the most dscrmnatng and fndng out the sgnfcant texture features that can dfferentate between thesetwotypesofabnormaltes,ncomparsontonormal. Haralck features are statstcal features that are computed over the entre mage. These measurements are utlzed to descrbe the overall texture of the mage usng measures

2 2 Internatonal Journal of Bomedcal Imagng (a) (b) (c) Fgure 1: (a) The lung CT mage; (b) the hstogram equalzed mage; (c) the Wener fltered output mage. such as entropy and sum of varance. Chaddad et al. propose an approach, based on Haralck s features, to detect and classfy colon cancer cells. Ths work amed to select the most dscrmnatng parameters for cancer cells [11]. A study to nvestgate the feasblty of usng Haralck features to dscrmnate between cancer and normal submages wthn a patent s llustrated n [12]. In ths paper, CT mages are frst preprocessed for nose reducton and mage enhancement, followed by segmentaton technques, as the tools to segment the lungs, and fnally Haralck texture features [13 15] are calculated. Statstcal analyss s done to detect the most sgnfcant Haralck features that wll characterze the type of the abnormalty wthn the lungs. Despte the low contrast and hgh nose exstence n the mages, the proposed algorthms ntroduce promsng results n detectng the abnormalty of lungs n most of the patents n comparson wth the normal. 2. Materals and Methods Ths paper presents a new automatc lung cancer detecton system based on Haralck texture features extracted from the slce of DICOM Lung CT mages. The proposed system s accomplshed n four stages: mage preprocessng, lung mage segmentaton, feature extracton, and classfcaton. Statstcalanalysssusedtoobtanthebestfeaturesfor classfcaton to dfferentate between lung cancer patents, ordered edema patents, and control subects. The followng sectons wll descrbe n detal these stages. All mage analyses were acheved wthout any knowledge of patent clncal characterstcs or status Dataset. Patents wth ether a lung cancer tumor or pulmonary edema were encompassed n the study. Ths study ncluded two datasets, the frst dataset referred to the Radology Department at New Elkasr ElAny teachng hosptal,unverstyofcaro.theotherdatasetwasobtaned from The Cancer Imagng Archve (TCIA) sponsored by the SPIE, NCI/NIH, AAPM, and the Unversty of Chcago [16]. The two datasets of 532 CT mages from 37 dfferent patents were ncluded. The mages are storednDICOM format. For each lung CT mage, we separate the left lung from the rght lung automatcally, and each separated lung s labeled as normal or edema/cancer based on the dataset nformaton Preprocessng. The man goal of preprocessng s to mprove the qualty of an mage as well as make t n a form suted for further processng by human or machne [17]. Ths s accomplshed by enhancng the vsual appearance of an mage besdes removng the rrelevant nose and unwanted parts n the background. The proposed enhancement process, whch s based on combnng flters and nose reducton technques for pre- and postprocessng as well, s carred out applyng hstogram equalzaton (HE) [18 20] followed by Wener flterng [21, 22]. Fgure 1 presents the enhancement n the lung mage contrast attaned by applyng the hstogram equalzaton. However, the obtaned gray scale mage contans noses such as whte nose and salt and pepper nose. Thus, Wener flter s utlzed to remove these noses from the enhanced lung mage. Fgure 1(c) shows the effect of applyng Wener flter on the contrast enhanced lung mage Lung Segmentaton. Lung segmentaton step ams to bascally extract the voxels correspondng to the lung cavty n the axal CT scan slces from the surroundng lung anatomy. The segmentaton technque proposed n [23] s utlzed. Ths technque s based on the fact that there s a large densty dfference between ar-flled lung tssues and surroundng tssues. Furthermore, both lungs are almost lookng lke mrror mages of themselves n a human body. The segmentaton of lung regons s acheved through the followng steps. In thefrststep,thepreprocessedctmagesconvertedntoa bnary mage; a threshold of 128 was selected. Values greater than the threshold are mapped to whte, whle others less than that are marked as black. Consequently, the two lungs are marked and the area around them s cropped out. Second, an eroson morphologcal operaton s employed n order to elmnate any whte pxels wthn the two lungs. Afterward,

3 Internatonal Journal of Bomedcal Imagng 3 (a) (b) (c) (d) (e) Fgure 2: (a) The threshold mage; (b) the eroded mage; (c) the lung mask mrror; (d) the mask proecton of the correspondng lungs mages; (e) the extracted lungs. the eroded and the orgnal mages are both dvded nto two equal regons. Black pxels for each regon n the eroded mage are counted; the regon wth the largest black area wll be deemed as a lung mask. The attaned lung mask s reflected n the opposte drecton. As a result, rght and left lungmasksareobtaned.thesemasksaremultpledwth the correspondng orgnal mage regons; ths wll proect the lung masks on the orgnal two lungs mages. Fnally, update each black pxel n the obtaned mages by ts orgnal value; other pxels are set to 255. Fgure 2 llustrates the lung extracton process Feature Extracton. Feature extracton s the process of obtanng hgher-level nformaton of an mage such as color, shape, and texture. Texture s a key component of human vsual percepton. Statstcal texture methods analyze the spatal dstrbuton of gray values, by computng local features at each pont n the mage and nferrng a set of statstcs from the dstrbutons of the local features. Haralck et al. ntroduced Gray Level Cooccurrence Matrx (GLCM) and texturefeaturesbackn1973[13].thstechnquehasbeen wdely used n mage analyss applcatons, especally n the bomedcal feld. It conssts of two steps for feature extracton. The GLCM s computed n the frst step, whle the texture featuresbasedontheglcmarecalculatednthesecondstep. GLCM shows how often each gray level occurs at a pxel located at a fxed geometrc poston relatve to each other pxel, as a functon of the gray level [13]. The horzontal drecton 00 wth a range of 1 (nearest neghbor) was used n ths work. The 9 texture descrptons used are presented n (4) to (13), where N g sthenumberofgraylevels,p d s the normalzed symmetrc GLCM of dmenson N g N g, and p d (, ) s the (, )th element of the normalzed GLCM [13]. Contrast (Moment 2 or standard devaton) s a measure of ntensty or gray level varatons between the reference pxel and ts neghbor. Large contrast reflects large ntensty dfferences n GLCM: Contrast = ( ) 2 p d (, ). Homogenety measures how close the dstrbuton of elementsntheglcmstothedagonalofglcm.ashomogenety ncreases, the contrast, typcally, decreases: 1 Homogenety = 1+( ) 2 p d (, ). (2) Entropy s the randomness or the degree of dsorder present nthemage.thevalueofentropysthelargestwhenall elementsofthecooccurrencematrxarethesameandsmall when elements are unequal: Entropy = p d (, ) ln p d (, ). Energy s derved from the Angular Second Moment (ASM). The ASM measures the local unformty of the gray levels. (1) (3)

4 4 Internatonal Journal of Bomedcal Imagng When pxels are very smlar, the ASM value wll be large. Consder Energy = ASM ASM = p 2 d (, ). (4) Correlaton feature shows the lnear dependency of gray level values n the cooccurrence matrx: Correlaton = p d (, ) ( μ x)( μ y ), (5) σ x σ y where μ x ; μ y and σ x ; σ y are the means and standard devatons and are expressed as μ x = μ y = σ x = σ y = p d (, ) p d (, ) ( μ x ) 2 p d (, ) ( μ y ) 2 p d (, ). The moments are the statstcal expectaton of certan power functons of a random varable and are characterzed as follows. Moment 1 (m 1 )sthemeanwhchstheaverageofpxel values n an mage and t s represented as m 1 = ( ) p d (, ). Moment 2 (m 2 ) s the standard devaton that can be denoted as m 2 = ( ) 2 p d (, ). Moment 3 (m 3 ) measures the degree of asymmetry n the dstrbuton and t s defned as m 3 = ( ) 3 p d (, ). And fnally Moment 4 (m 4 ) measures the relatve peak or flatness of a dstrbuton and s also known as kurtoss: m 4 = ( ) 4 p d (, ). (6) (7) (8) (9) (10) Furthermore, dfference statstcs that are a subset of the cooccurrencematrxarealsoused.thesefeaturesarebased on the dstrbuton of probablty P x y (k) whch s defned as follows: P x y (k) = C d (,), k=0,1,...,n g 1, (11) where C d (, ) s the (, )th element of the GLCM. The most basc dfference statstc texture descrptons are the ASM, mean, and entropy: ASM = (P x y (k)) 2. (12) k When the P x y (k) values are very smlar or close, ASM s small. ASM s large when certan values are hgh and others are low: Mean = kp x y (k). (13) k When P x y (k) values are concentrated near the orgn, mean s small and mean s large when they are far from the orgn: Entropy = P x y (k) log P x y (k). (14) k Entropy s smallest when P x y (k) values are unequal and largest when P x y (k) values are equal. The calculaton of the Haralck texture features usng the prevous equatons for the CT mages volume sequences foreverysegmentedlung(rghtandleft)separatelywas performed. For each partcpant the gray level cooccurrence texture features: contrast, homogenety, entropy, energy, correlaton, and m 1, m 2, m 3,andm 4 accompaned by the dfference statstcal features: ASM, contrast, mean, and entropy were obtaned for each segmented lung (rght and left) Statstcal Analyss. For the purpose of random lung assgnment n healthy volunteers, the left lung represented the dseased lung n the same percentage of cases as the patent populaton. For the acute data, two sngle factor analyses of varance (ANOVA) tests were conducted for each Haralck texture feature measurement between affected (ether left or rght) and fellow lung (ether left or rght) for both categores cancer and edema patents. A sngle factor analyss of varance (ANOVA) was conducted as well between patents and controls. Other between-subect sngle factor analyses were conducted to fnd out the sgnfcant Haralck features that could dfferentate cancer from edema. 3. Expermental Results Two datasets of 532 CT mages were ncluded. For each lung CT preprocessed mage, we separate the left lung from the rght lung automatcally as dscussed before n Secton 2.3, and each separated lung s labeled as normal or edema/cancer based on the dataset nformaton. The Haralck texture features measurements for each lung separately are calculated (the gray level cooccurrence texture features: contrast, homogenety, entropy, energy, correlaton, and moments along wth the dfference statstcal features: ASM, mean, and entropy). The mean and the standard devaton of the Haralck texture features measurements calculated as well as the ANOVA results are gven for tumor patents affected lung versus fellow lung n Table 1 and for pulmonary edema patents n Table 2. The ANOVA summary of statstcs for ether pulmonary

5 Internatonal Journal of Bomedcal Imagng 5 Table 1: ANOVA (1 wthn-subect factor) results for cancer patents Haralck texture features (comparson between AL and FL). AL: affected lung; FL: fellow lung. Feature name AL (average ± SEM) FL (average ± SEM) AL versus FL Homogenety ± ± 0.01 F(1, 426) = 22.0 p< Energy ± ± 0.01 F(1, 426) = 15.1 p< Correlaton ± ± F(1, 426) = 6.15 p < Contrast ± ± 4.54 F(1, 426) = p < Entropy 8.0 ± ± 0.19 F(1, 426) = 11.8 p< m ± ± 0.02 F(1, 426) = p < 0.88 m ± ± 4.01 F(1, 426) = p < m ± ± F(1, 426) = 2.65 p < 0.09 m ± ± F(1, 426) = 6.25 p < 0.19 Dff ASM ± ± F(1, 426) = 3.18 p < 0.06 Dff Mean ± ± 0.09 F(1, 426) = 2.16 p < 0.12 Dff Entropy ± ± 0.03 F(1, 426) = 2.32 p < 0.10 Table 2: ANOVA (1 wthn-subect factor) results for edema patents Haralck texture features (comparson between AL and FL). AL: affected lung; FL: fellow lung. Feature name AL (average ± SEM) FL (average ± SEM) AL versus FL Homogenety 0.64 ± ± F(1, 105) = 2.16 p < 0.15 Energy ± ± 0.01 F(1, 105) = p < 0.87 Correlaton ± ± F(1, 105) = 2.32 p < Contrast ± ± 4.26 F(1, 105) = 15.1 p< Entropy 2.10 ± ± F(1, 105) = 1.28 p < m ± ± 0.02 F(1, 105) =41.8 p< m ± ± F(1, 105) = 2.20 p < m ± ± 885 F(1, 105) =41.8 p< m ± ± F(1, 105) = 2.12 p < Dff ASM ± ± 0.08 F(1, 105) =4.56 p < Dff Mean 4.07 ± ± F(1, 105) = 7.87 p < 0.01 Dff Entropy 2.96 ± ± 0.05 F(1, 105) = 4.73 p < edema or tumor patents versus normal s gven n Table 3. The sgnfcant Haralck texture features that can dfferentate between pulmonary edema and tumor are found n Table 4. From Table 1, we can conclude that Haralck texture features measurements (homogenety, energy, correlaton, and entropy) of the affected cancer lung were sgnfcantly dfferent than that of the fellow lung. The homogenety, energy, and correlaton were sgnfcantly less than those of thenormalfellowlung.whleentropyofthecancerouslung s approachng beng sgnfcantly more than that of the fellow lung, Moment 3 and the dfference statstcal feature ASM (dff ASM) texture feature measurement of the cancerous lung s approachng beng sgnfcantly less than that of the normal lung. Table 2 showed that Haralck texture features measurements (homogenety, entropy, and moments calculated from the cooccurrence matrx as well as mean and ASM computed from the dfference statstcs) of the pulmonary edema affected lung were also sgnfcantly dfferent than those of the control subect lung; moreover contrast and entropy computed from the dfference statstcs were sgnfcantly more than those of the fellow lung. Consderng Tables 1 and 2, we can conclude from Table 3 that the homogenety, energy, entropy, m 3, m 4,dffASM, dff mean, and dff entropy are good bomarkers to sgnfcantly dfferentate between dseased and normal lungs wthout any dsease specfcaton. On the other hand, the results llustrated n Tables 1, 2, and 4 show that entropy and the entropy calculated from the dfference statstcs would be a good canddate to sgnfcantly dfferentate between pulmonary edema and cancer. 4. Concluson and Dscusson The texture features analyses are well known approaches to quantfy and express the heterogenety that may not be apprecated by clncal naked eyes, and t was presented before as good magng bomarkers to dfferentate between dseases. InthspaperanevaluatonoftheHaralcktexturefeatures s done n order to dentfy the most sgnfcant features that can be used n order to detect and dfferentate abnormaltes wthn the lungs for cancer and edema versus normal. Our results ndcate that entropy determned by gray level cooccurrence matrx and ASM s sgnfcantly dfferent n

6 6 Internatonal Journal of Bomedcal Imagng Table 3: ANOVA (1 wthn-subect factor) results summary of statstcs p value for patents (ether edema or cancer) Haralck texture features versus normal controls. Feature name Dseased versus normal controls (p value) Feature name Dseased versus normal controls (p value) Homogenety p < m 2 p < Energy p < m 3 p < Correlaton p < m 4 p < Contrast p < Dff ASM p < Entropy p < Dff Mean p < m 1 p < Dff Entropy p < Table 4: ANOVA (1 between-subect factor) results summary of statstcs p value for patents Haralck texture features cancer versus edema patents. Feature name Cancer versus edema patents (p value) Feature name Cancer versus edema patents (p value) Homogenety p < m 2 p < 0.69 Energy p < m 3 p < 0.01 Correlaton p < m 4 p < 0.89 Contrast p < ASM p < 0.73 Entropy p < Mean p < m 1 p < Entropy p < edema patents versus normal whle t s not n cancer patents versus normal. Snce the entropy s the degree of randomness or the degree of dsorder n the mage, and the angular second moment represents the unformty n the mage, ths may be nterpreted as the cancer dsease causng a localzed heterogenety n the dseased specfed area n the lung whle the edema causes heterogeneous dsorder n the whole lung mage. Hgh entropy values calculated mples that the elevated level of dsorder and dsorganzaton occurred due to the edema dseased lung versus the cancer dseased lung. The energy feature that s derved from the angular second moment measures and representng the local unformty of the gray levels s a good bomarker to dfferentate between cancer and edema dseases. From Table 2, contrast s a good bomarker for the pulmonary edema dsease and ths agrees wth the texture feature meanng whch means hgh contrast values for heavy texture changes. Gray level cooccurrence matrx textural propertes such as homogenety, correlaton, mean, and moments are good sgnfcant bomarkers for dseased lung versus normal ones n general wthout any specfcaton for the dsease type. Our results agree wth other artcles ndcatng that textural analyss has the potental to develop nto a valuable clncal tool that mproves the dagnoss, tumor stagng, and therapy assessment. Whle our results are promsng, there s stll further work that can be done n the detectng of the abnormalty wthn the lungs to detect the type of that abnormalty whether t wll be a lung cancer or edema. A prelmnary nvestgaton has been done usng statstcal analyss to dentfy the most useful texturefeaturesthatcanbefedtoanyclassfcatontechnque later. Ths statstcal analyss s done usng ANOVA. After selectng these features we can feed them for better localzaton and classfcaton as further work. Conflct of Interests The authors declare that there s no conflct of nterests regardng the publcaton of ths paper. Acknowledgments The authors acknowledge the SPIE, the NCI, the AAPM, and The Unversty of Chcago for provdng publc access to the lung cancer dataset. References [1] L.B.WareandM.A.Matthay, Acutepulmonaryedema, The New England Journal of Medcne, vol.353,no.26,pp , [2] J. Šedý, J. Zcha, J. Kuneš, P. Jendelová, and E. Syková, Mechansms of neurogenc pulmonary edema development, Physologcal Research,vol.57,no.4,pp ,2008. [3] R. S. Herbst, J. V. Heymach, and S. M. Lppman, Lung cancer, The New England Journal of Medcne,vol.359,no.13,pp , [4] N. Hollngs and P. Shaw, Dagnostc magng of lung cancer, European Respratory Journal,vol.19,no.4,pp ,2002. [5] T. Mankandan and N. Bharath, Lobar fssure extracton n sotropc CT lung mages an applcaton to cancer dentfcaton, Internatonal Journal of Computer Applcatons,vol.33,no. 6, pp , [6] J. Wang, F. L, and Q. L, Automated segmentaton of lungs wth severe ntersttal lung dsease n CT, Medcal Physcs, vol. 36, no.10,pp ,2009. [7] U. Baĝc,M.Bray,J.Caban,J.Yao,andD.J.Mollura, Computerasssted detecton of nfectous lung dseases: a revew, Computerzed Medcal Imagng and Graphcs, vol.36,no.1,pp.72 84, 2012.

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