Detection of Pulmonary Nodules in Low dose Computed Tomography Using Localized Active Contours and Shape Features

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1 Originl Article Detection of Pulmonry Nodules in Low dose Computed Tomogrphy Using Loclized Active Contours nd Shpe Fetures Astrct Bckground: Pulmonry nodules re symptoms of lung cncer. The shpe nd size of these nodules re used to dignose lung cncer in computed tomogrphy (CT) imges. In the erly stges, nodules re very smll, nd rdiologist hs to refer to mny CT imges to dignose the disese, cusing opertor mistkes. Imge processing lgorithms re used s n id to detect nd loclize nodules. Methods: In this pper, novel lung nodules detection scheme is proposed. First, in the preprocessing stge, our lgorithm segments two lung loes to increse processing speed nd ccurcy. Second, templte mtching is pplied to detect the suspicious nodule cndidtes, including oth nodules nd some lood vessels. Third, the suspicious nodule cndidtes re segmented y loclized ctive contours. Finlly, the flse positive errors produced y vessels re reduced using some two /three dimensionl geometricl fetures in three steps. In these steps, the size, long nd short dimeters nd sphericity re used to decrese the flse positive rte. Results: In the first step, some vessels tht re prllel to CT cross plne re identified. In the second step, olique vessels re detected using shift of center of grvity in two successive slices. In step three, vessels verticl to CT cross plne re identified. Using these steps, vessels re seprted from nodules. Erly Lung Cncer Action Project is used s populr dtset in this work. Conclusions: Our lgorithm chieved sensitivity of 90.1% nd specificity of 92.8%, quite cceptle in comprison to other relted works. Zhr Ndelin, Behzd Nzri, Seid Sdri, Mohmmd Momeni 1 Deprtment of Electricl nd Computer Engineering, Isfhn University of Technology, 1 Deprtment of Rdiology, Alzhr Hospitl, Isfhn University of Medicl Sciences, Isfhn , Irn Keywords: Computer ided detection, computed tomogrphy imges, feture extrction, loclized ctive contours, pulmonry nodules, templte mtching Introduction Lung cncer in the erly stges hs no symptoms in seril computer tomogrphy (CT) imges, wheres its erly detection cn increse survivl rte. The symptoms of lung cncer only pper in more serious stges due to chrcteristics nd position of the nodules in the lungs. [1] Lung nodules re round or egg shped lesions with regulr or irregulr oundries. Nodules in erly stges re very smll, thus high qulity imges need to e used, nd these imges need to e processed y rdiologist visully. In such sitution, computer ided detection (CAD) systems process the CT imges to ssist the rdiologist in detecting pulmonry nodules nd incresing sensitivity nd specificity. Suspicious res re minly due to lood vessels which resemle nodules. In the reminder of this section, two ctegories of ppers re investigted. The first ctegory discusses nodule detection, [2 16] This is n open ccess rticle distriuted under the terms of the Cretive Commons Attriution-NonCommercil-ShreAlike 3.0 License, which llows others to remix, twek, nd uild upon the work non-commercilly, s long s the uthor is credited nd the new cretions re licensed under the identicl terms. For reprints contct: reprints@medknow.com nd the second group considers nodule segmenttion. [17 23] Murphy et l. [2] descried scheme for the utomtic detection of nodules in thorcic CT scns tht enhnces flse positive reduction using two successive k nerest neighor clssifiers. Liu et l. [3] proposed computer ided dignosis system for lung nodule detection using the hidden conditionl rndom field. This method chieved sensitivity of 89.3%. Keserci nd Yoshid [4] proposed new computer ided dignosis scheme for utomted detection of lung nodules in digitl chest rdiogrphs sed on comintion of morphologicl fetures nd wvelet snke model. Choi nd Choi [5] presented n lgorithm sed on two dimensionl (2D) nd three dimensionl (3D) fetures to reduce flse positive errors. This system first enhnced the CT imges nd then detected nodule cndidtes. Afterwrd, the fetures re extrcted from every region of interest (ROI). The 3D geometric feture How to cite this rticle: Ndelin Z, Nzri B, Sdri S, Momeni M. Detection of pulmonry nodules in low-dose computed tomogrphy using loclized ctive contours nd shpe fetures. J Med Sign Sens 2017;7: Address for correspondence: Ms. Zhr Ndelin, Deprtment of Electricl nd Computer Engineering, Isfhn University of Technology, Isfhn , Irn. E mil: z.ndelin@ec.iut.c.ir Access this rticle online Wesite: DOI: /jmss.JMSS_71_16 Quick Response Code: 2017 Journl of Medicl Signls & Sensors Pulished y Wolters Kluwer - Medknow 203

2 Ndelin, et l.: Detection of pulmonry nodules set consisted volume elongtion fctor, compctness, nd pproximted rdius. In ddition, the 2D fetures were extrcted from middle slice of nodule cndidtes. These fetures were men, vrince, skewness, kurtosis, re, rdius, nd eight iggest eigenvlues. Finlly, these fetures were pplied s input to genetic progrmming module. The method pplied to 32 scns consisting of 153 nodules nd 7528 slices. Hlf of dtset (16 scns) ws used for trining, nd the remining ws used to test the clssifier. This method chieved sensitivity of 92% with n verge of 6.5 flse positives per scn. Suzuki et l. [6] investigted pttern recognition technique sed on n rtificil neurl network for the reduction of flse positives of detected lung nodules in low dose CT. Go et l. [7] proposed novel templte mtching using Hessin mtrix in series of chest X ry imges. First, the lung loes were extrcted in the originl imges. Then, templte mtching method ws used to detect the suspicious nodule cndidtes. Although it ws fst nd nerly ll nodules were detected, it hd high flse positive rte. Hence, to clssify nodule cndidtes into two groups, nodules nd vessel, clssifier sed on eigenvlues of Hessin mtrix ws proposed. The lgorithm depended on few constnts tht hd to e specified experimentlly. This method ws pplied to oth high resolution CT nd to norml CT. Yognnd et l. [8] proposed n lgorithm to detect the pulmonry nodules. After preprocessing step, morphologicl opertions were used to detect the nodule cndidtes. Then, to discriminte etween the nodules nd the vessels, the successive frmes of the CT were exmined. If the cndidte ws vessel, the displcement of center of grvity of the cndidte in successive frmes ws lrge. However, if the cndidte ws nodule, the displcement would e smll. This lgorithm filed to seprte the nodules from verticl vessels. Nmin et l. [9] developed n utomtic method for lung nodule detection. After preprocessing step, suspicious nodule cndidtes were detected on the sis of intensity nd volumetric shpe index (SI). This method chieved sensitivity of 88% with n verge of 10.3 flse positives per scn. Lin et l. [10] developed neurl network system sed on two level convolution neurl network rchitecture to reduce flse positives. Shi et l. [11] employed neurl network ensemle for flse positives reduction in detecting lung nodules in chest rdiogrphs. The performnce of this lgorithm ws evluted y use of FROC. Dolejsi et l. [12] designed CAD system oth to detect smll pulmonry nodules efficiently nd to reduce flse positives sed on Asymmetric Adoost ensemle. Guo et l. [13] proposed scheme on the sis of n dptive clssifiction system. Eight fetures were extrcted from nodule cndidtes, nd support vector mchine (SVM) ws used s nonliner clssifier. Assef et l. [14] proposed n lgorithm using templte mtching nd multiresolution nlysis to improve flse positive rte. In this lgorithm, intensity nd multiresolution fetures were used. This method chieved detection rte of % nd flse positive rte of 35.15%. Frg et l. [15] determined the models for lung nodule detection y templte mtching method. This lgorithm emphsized on the fetures using texture nd shpe properties of nodules. de Crvlho Filho et l. [16] proposed n lgorithm sed on txonomic diversity nd txonomic distinctness indexes from ecology to descrie the texture of nodule nd nonnodule cndidtes. Then, SVM ws used s clssifier. This lgorithm chieved men ccurcy of 98.11%. By considering the ovementioned ppers, there re generlly two pproches in nodule detection: detection using only single 2D frme nd detection using series of 2D frmes. As it is heuristiclly cler, experiments show tht detection results re much etter, when multiple frmes re used for dignosis. Furthermore, the experimentl results hve shown tht ccurte segmenttion of regions in CT imges hs significnt effect in the dignosis of pulmonry nodules. Therefore, mny lgorithms hve een developed for this purpose. [17 23] In the following, rief explntion of lgorithms used for lung nodule segmenttion is presented. Messy et l. [18] used regression neurl network to determine threshold prmeters in lung nodule segmenttion. They tested their lgorithm on 66 lung nodules nd chieved segmenttion ccurcy of 80%. Qin nd Guirong [20] proposed the expecttion mximiztion (EM) lgorithm for lung nodule detection nd segmenttion. Their model ws independent from the size of nodules. Sun et l. [21] developed n utomtic segmenttion lgorithm of nodules in CT imges. This method successfully distinguished nodules ttched to pleurl surfce, ut it filed to segment the cvity nodules. Zinovev et l. [22] proposed soft segmenttion sed on pixel level texture fetures using decision tree clssifier with clssifiction nd regression tree lgorithm. This method successfully distinguished the nodules tht were ttched to vessels, ut it filed to segment those ttched to the chest wll. To solve this prolem, the lung segmenttion ws performed in the preprocessing step. Okd nd Akdemir [23] proposed segmenttion method using SI. This lgorithm provided successful results to segment the sphericl nodules, ut it filed to detect the nonsphericl nodules. The model used in this pper successfully segmented ground glss opcity (GGO) nodules, cvity nodules, nd nodules tht re ttched to vessels or chest wll. To chieve high performnce level for lung nodules detection, n efficient method employs three mjor steps: initil nodule detection, segmenttion process, nd flse positive reduction. In this study, we employed templte mtching (TM) method to increse the detection sensitivity. Then, we utilized locl ctive contour to 204 Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer 2017

3 Ndelin, et l.: Detection of pulmonry nodules increse segmenttion ccurcy; this method is le to segment chllenging nodule cndidtes (vessels, different types of nodules including GGO nodules, cvity nodules, sphericl nd nonsphericl nodules, nd nodules tht re ttched to vessels or chest wll) tht some previous works fil. Finlly, we proposed three step flse positive reduction lgorithm; this pproch ws used to distinguish nodules from other suspicious regions including olique lood vessels nd vessels tht re prllel or verticl to CT cross plne. The min novelties of this work re utomted nodule detection long with discriminting nodules from different type of vessels using shpe fetures, which leds to high sensitivity nd specificity. Figure 1 gives n overll overview of our scheme for nodule detection in CT imges. Accordingly, the reminder of this pper continues s follows: our proposed lgorithm is presented in Section 2. In Section 3, we discuss the experimentl results. Conclusion is given in Section 4. Mterils nd Methods Preprocessing process Nodule detection process is divided into the following steps: preprocessing, identifiction of suspicious nodule cndidtes, clssifiction, nd flse positive reduction. In the preprocessing stge, the lung loes re segmented in CT imges. The pproch proposed in [24] is used to segment the lung from its surrounding sed on EM lgorithm nd morphologicl opertions. Furthermore, contrst djustment Reduction of flse positive findings y three steps Input CT scn imge Preprocessing process Detection of suspicious nodule cndidtes Segmenttion process Detection of vessels prllel to CT cross-plne Detection of olique lood vessels Detection of vessels verticl to CT cross plne Declrtion of detected nodules Figure 1: Flowchrt for nodule detection procedure is pplied to improve the qulity of the lung loe imges. A smple is shown in Figure 2. Detection of suspicious nodule cndidtes TM cn e used to detect the pulmonry nodules efficiently, ut t the sme time mny vessels re lso detected s nodules in this mnner. [7] Intensity vlue of nodules usully hs Gussin like distriution s follows: [15] mx 2 -( r / ρ ) qr ( )= q * e,0 r R (1) Where, ( ( ) ( )) 0.5 mx min ρ = R ln q ln q In which q min nd q mx re the minimum nd mximum of nodule intensity, q(r) is the intensity of nodule t distnce from the centroid of the nodule, nd R is the rdius of nodulr templte. TM method computes the normlized cross correltion (NCC) of the imge nd the templte y the following eqution: γ = Where x,y x,y ( ) f( xy, ) f u,v tx uy, v t ( ) 2 2 f( xy, ) f u,v tx uy, v t x,y x y 1 fu,v = f(x, y) NN x y u+n 1 v+n 1 x=u y=v f (x,y) nd f re the input imge nd its men vlue u,v in the region under templte locted t (u,v), t(x,y), is the nodule model nd its men vlue is t. In (2), the correltion coefficient γ vries etween 1 nd 1. If the correltion coefficient is γ ove positive threshold level, the point (u,v) is ccepted to elong suspicious nodule cndidte. [7] This threshold level ccording to our dtse is experimentlly set to 0.6. In our experiments, we used lirry of circulr nd semi circulr templtes with 90 rottions with Gussin distriution of gryscle intensity. The men dimeter of nodules in our dtse is 8.5 mm with stndrd devition of 3.6 mm. Accordingly, we chose dimeter of 8.5 mm for our circulr nd semi circulr templtes the sme s. [15] Using this method, mny regions re tgged, ut the true positive rte is high. Figure 3 shows smple of this method. The templte imge is seen s Figure 3. After removed interference, the pulmonry prenchym imge is gotten y est weight segmenttion nd seen s Figure 3. The normlized cross correltion of the imge nd the templte is seen s Figure 3c nd the initil regions of interest re shown s Figure 3d. It should e noted tht in the ove procedure, some vessels re lso detected s suspicious nodule cndidtes. (2) Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer

4 Ndelin, et l.: Detection of pulmonry nodules 2D processing is frequently not le to remove the interference of vsculr, leding to high flse positive rte. To seprte the nodules from vessels in the next section, nodule cndidtes re segmented, nd then these segmented nodule cndidtes re clssified using some fetures. Segmenttion process Accurte segmenttion of suspicious nodule cndidtes, tht could e nodule or vessel, is very importnt. Segmenttion of nodule cndidtes is chllenging ecuse of the following three effects: noise, nodules tht re ttched to lood vessels or ttched to lung wll, nd low contrst of intensity vlues etween nodules nd other structures in CT imges, due to low dose CT imging. The sic ide in ctive contour models is to llow contour to deform so s to minimize given cost function to provide the desired segmenttion results. In generl, there re two min ctegories of ctive contour models: edge sed nd region sed. Edge sed ctive contour models minly utilize the edge nd grdient informtion to drive the contours to identify oject oundries. The result of imge segmenttion y these models is highly dependent on the initil contour plcement nd very sensitive to imge noise. Compred with edge sed ctive contours, region sed ctive contours model the foreground nd ckground regions sttisticlly nd optimize glol energy cost function. These models re less sensitive to initiliztion nd imge noise. Region sed ctive contours focus on loclized energy function sed on piece wise constnt model of Chn nd Vese (C V model), [25] which cn e written s: F( u,v, φ) = µ H ( φ ) dx +ν H ( φ) dx + λ Ω Figure 2: Lung loe segmenttion with contrst djustment. () Input imge, () msk of segmented lung region, (c) segmented lung H ( φ)( I - u) dx + λ (1- H ( φ))( I - v) dx (3) Ω Ω Ω Where I is given imge defined on domin Ω, υ, nd re v the glol men intensities of the interior nd exterior regions. λ 1, λ 2, ν nd µ re positive constnt coefficients nd is H ( φ) Heviside function. c c Optimizing glol sttistics usully is not idel for segmenting heterogeneous ojects. [26] Therefore, to ccurtely segment these ojects, new model of ctive contour is developed which utilizes locl informtion. Loclized ctive contours re cple of segmenting ojects with heterogeneous feture profiles. [27] In this pproch, segmenttion is not sed on glol region models. The verge intensities in interior nd exterior res of msk B (x,y) re computed t ech point. To optimize the totl energy of the contour, the msk is considered in ech point seprtely, nd the point is moved to decrese the energy function. This energy function is defined s following: E( φ) = δφ( x) B( x, y ).F( I( y), φ( y)) dxdy Ωx Ωx + λ δφ( x) φ( x) dx F is generic internl energy, nd δφ( x ) is Dirc function. Using B (x,y), F opertes only on locl imge informtion in neighorhood of (x,y) nd λ is smoothing prmeter. Using clcultion of vrition results: ϕ ( x) = δφ( x) (, ) δφ( ). B x y y t 2 2 φ( x) (( I(y) ux) ( I(y) vx) ) dy + λδφ( x) div( ) (5) φ( x) The loclized equivlents of nd tht defined in terms of the B (x,y) function, u x nd v x : u x = B(x, y). Hφ( y).i( y) dy Figure 3: Suspicious nodule cndidte detection. () Templte, () originl imge, (c) normlized cross correltion of imge nd templte, (d) result of templte mtching method d (4) B(x, y). Hφ( y) dy (6) 206 Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer 2017

5 Ndelin, et l.: Detection of pulmonry nodules v x = B(x, y).(1 Hφ( y)).i( y) dy B(x, y).(1 Hφ( y)) dy (7) Locl ctive contour hs three prmeters, mximum itertions which is set to 200, locl rdius (B (x,y)) set to 1 nd smoothing prmeter (λ) set to 0.2. The prmeters re chosen ccording to nodule specifictions experimentlly. Figure 4 compres the results of glol nd locl ctive contours pplied to segmenttion of nodule. It cn e seen tht locl ctive contour successfully segmented the nodule cndidtes with concve nd low contrst oundries. This model ws tested on different kinds of nodule cndidtes including oth nodules nd vessels. The experiments show the roustness nd reliility of the model. As shown in Figure 5, this method successfully c Figure 4: ( nd c) Initiliztion, () finl result with glol energies, (d) finl result with locl energies d segmented nodule cndidte regions from djcent structures in mjority of cses ut filed to discriminte etween ckground nd some vessels. Figure 6 shows vessel segmented y loclized ctive contour. As it is shown in this figure, some gps re generted within vessels. It should e noted tht such filure mkes no prolem in the finl results ecuse vessels re identified from nodules nd omitted in lter stges. Discriminting lood vessels from nodules In ech CT cross section, some vessels my resemle nodules, cusing flse nodule cndidtes which must e identified nd omitted. The following three steps do the jo. Nonnodule ojects cn e discriminted from nodule cndidtes using informtion of nodules such s size, volume, sphericity, nd compctness. Detection of vessels prllel to computed tomogrphy cross plne Geometricl shpes of pulmonry nodules nd vessels re sphericl nd cylindricl, respectively. A slice in sphere, semi sphere, verticl, or nerly verticl cylinder is circulr or nerly circulr. As the cylinder inclines more prllel to CT cross plne, the difference etween the long dimeter nd the short dimeter increses. The long dimeter is the distnce etween two points of nodule oundry with mximum distnce, nd the short dimeter is the longest chord perpendiculr to the long dimeter. We use long dimeter/short dimeter s feture to clssify vessels nd nodules. The rtio is lrge for olique vessels or vessels verticl to CT cross plne nd is smll for nodules. The digrms in Figure 7 show the distriution of long dimeter/short dimeter for nodules nd vessels. The digrm verticl xis is the numer of nodules/vessels with specific long dimeter/short dimeter of horizontl xis. The threshold vlue is experimentlly set to 2.1. Some of the vessels tht re detected or not detected in this step c d e f g Figure 5: Segmenttion results. ( nd c) re nodule nd (e nd g) re vessel tht re shown y lue rrows, (, d, f nd h) re the contour of the segmenttion h Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer

6 Ndelin, et l.: Detection of pulmonry nodules re shown in Figure 8. In the next steps, remining nodule cndidtes re exmined to detect other possile vessels. Detection of olique lood vessels If we consider the center of grvity of nodule or verticl vessel in two successive slices, there would e smll displcement etween these two centers [Figure 9], wheres this displcement would e greter for n olique vessel. The digrms in Figure 10 show the distriutions of these displcements for nodules nd vessels. Hence, numer of olique lood vessels in nodule cndidtes cn e identified using threshold on these displcements. The digrm verticl xis is the numer of nodules/vessels with specific displcements of horizontl xis. The threshold vlue is experimentlly set to Detection of vessels verticl to computed tomogrphy cross plne In the previous two steps, olique nd prllel vessels were removed from the suspicious nodule cndidte list. In the current step, we will only consider the remining suspicious nodule cndidtes. A vessel is not compct oject nd continues to e connected in consecutive slices of CT Figure 6: Prts of the vessel similr to ckground re shown inside lue circles nd the contour of the segmenttion is shown in green imges. [5] On the contrry, nodule s shown in Figure 11 is compct nd sphere oject, so its cross section on CT slice is nerly semi circle. Experimentl results indicted tht there is smll difference etween the length nd short dimeter of nodule, wheres the difference would e higher for vsculr res ecuse vsculr res continues to e connected in successive slices of 2D CT imges. Thus, the length nd short dimeter of remining suspicious nodule cndidtes re extrcted s fetures. Furthermore, the length of nodule cndidtes is clculted s follows: Length = n slice thickness (8) n is the numer of slices tht trnsverses nodule cndidte. In this study, slice thickness of dt is mm. To determine, the following lgorithm is pplied s in Figure 12: 1. Three slices, the current slice nd slices efore nd fter it re considered 2. The cross section re of nodule is lredy segmented y locl ctive contour 3. Its reltive re in previous nd next frmes re mrked, nd the rightest pixels in these mrked res re determined nd leled y points P 1 nd P 2, respectively 4. P 1 nd P 2 re selected s the center of the initil locl ctive contours 5. The rdius of the ctive contours is 2. The following cses my occur s shown in Figure 12 nd c. 1. Ech contour in previous/next slice hs just one region. M p1 M p2 is the center of grvity in this region. If the following two conditions re fulfilled, the cndidte re is considered to elong to the current nodule cndidte. The verge intensity in this region e greter thn threshold. The displcement of its center of grvity with respect to the center of grvity of the current slice, shown with vectors d p1 d p2 e smller thn threshold 2. If the region segmented in the previous/next slice hs multiple res or hs just one pixel, s in Figure 12c, Figure 7: Histogrms of long dimeter/short dimeter for () nodules, () vessels 208 Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer 2017

7 Ndelin, et l.: Detection of pulmonry nodules the previous/next slice does not elong to the current nodule cndidte. Figure 8: () Blue rrows show vessels detected in section 2.4.1, () Blue circles show vessels which resemle nodules ut not detected in section Figure 9: Vessel shown inside the lue circles in consecutive six computed tomogrphy slices, verticl to computed tomogrphy cross plne According to the ove lgorithm, the totl numer of slices elonging to the nodule n nd so its length is determined. The digrms in Figure 13 show the sctter plot of the short xis dimeter nd length for nodules nd vessels. Using the fetures extrcted from the regions, the length nd short xis dimeter of the nodule cndidtes, SVM is used to clssify the nodules nd vessels. In experimentl results, it is shown tht SVM RBF improves the performnce of clssifiction. This clssifier is evluted y 5 fold cross vlidtion. Experimentl Results In this pper, we use the Erly Lung Cncer Action Progrm pulic dtse, which is one of the lung imge dtse consortium groups. This dtse contins fifty sets of low dose CT lung scns tken t single reth hold with slice thickness 1.25 mm. The loctions of nodules were certified y four rdiologists. Accordingly, 31% of nodules re solitry, 30% re ronchiole ttched, nd 39% re lung wll ttched, where 39.12% re juxtpleurl nodules, 13.95% re vsculrized nodules, 31.29% re well circumscried nodules, nd 15.65% re pleurl til nodules. The men dimeter of nodules in this dtse is 8.5 mm with stndrd devition of 3.6. [28] It includes 50 sets of low dose CT scns with totl of slices. CT scn imges of 20 ptients were used to develop the lgorithm, nd the remining imges were used to test the lgorithm. The numer of slices for ptient vries from 212 to 304. Slice thickness is 1.25 mm in this dtse, nd pixel spcing is from mm to mm for different ptients. At first, we used TM method to find nodule cndidtes, nd then we segmented the re of nodule cndidtes using loclized ctive contours. Then most of the lood vessels were removed from the nodule cndidte list ccording to the three steps in section 2.4. The prmeters of ll steps of the lgorithm re chosen experimentlly ccording to nodule specifictions in CT imges of our dtse. Figure 10: The histogrm of the displcement etween two centers for () nodules, () vessels Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer

8 Ndelin, et l.: Detection of pulmonry nodules Sensitivity nd specificity of our lgorithm were 90.1% nd 92.8%, respectively. Finlly, the results were compred with some of the ltest similr reserches on the suject s shown in Tle 1. In this tle, the highest sensitivity is in, [29] which is etter thn 90.1 of our lgorithm, ut our specificity is 92.8, etter thn in. [29] This result shows tht, compred with existing lgorithms, our method performs t similr or etter level. The receiver operting chrcteristic (ROC) curve is shown in Figure 14. ROC nlysis ws used to evlute the utomted clssifier. The re under the ROC curve is Figure 11: Feture prmeters of nodule Tle 1: Comprison etween our method with relevnt studies Method Sensitivity (%) Specificity (%) Dtse Numer of cses Frg ELCAP 50 et l. [15] Orozco ELCAP 75 et l. [29] nd NBIA Assef ELCAP 50 et l. [14] Our lgorithm ELCAP 30 NBIA Ntionl iomedicl imging rchives; ELCAP Erly Lung Cncer Action Project Figure 12: () Active contour of current slice nd the two corresponding regions in previous nd next slices. () Single regions segmented y locl ctive contours in previous nd next slices. (c) Multiple regions or one pixel c Figure 13: Sctter plot for short dimeter versus length for () nodules, () vessels 210 Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer 2017

9 Ndelin, et l.: Detection of pulmonry nodules Figure 14: Receiver operting chrcteristic curve of proposed method good mesure of clssifier performnce. In this work, the re under the ROC curve ws Our lgorithm ws run on Core i5, 2.53 GHz CPU nd 4G of RAM. The segmenttion time of suspicious nodule cndidte ws 5.0 s. Conclusion In this pper, new lgorithm for lung nodule detection ws proposed. At first, TM method ws used for detection of suspicious nodule cndidtes. Suspicious nodule cndidtes included oth nodules nd lood vessels. The nodule cndidte regions were segmented, nd then lood vessels were removed from the cndidte list. This lgorithm ws evluted on dtset of fifty thorcic CT scns. Our method is efficient for ccurte detection of lung nodules in CT imges. We compred the detection rte with previous existing methods; the proposed method performs similr or etter. Loclized ctive contour model provided superiority of our lgorithm. Furthermore, our lgorithm decresed flse positive rte using three step procedure using shpe fetures. Finncil support nd sponsorship None. Conflicts of interest There re no conflicts of interest. References 1. vn Ginneken B, ter Hr Romeny BM, Viergever MA. Computer ided dignosis in chest rdiogrphy: A survey. IEEE Trns Med Imging 2001;20: Murphy K, vn Ginneken B, Schilhm AM, de Hoop BJ, Gietem HA, Prokop M, et l. A lrge scle evlution of utomtic pulmonry nodule detection in chest CT using locl imge fetures nd k nerest neighour clssifiction. Med Imge Anl 2009;13: Liu Y, Wng Z, Guo M, Li P. Hidden Conditionl Rndom Field for Lung Nodule Detection. In: 2014 IEEE Interntionl Conference on Imge Processing (ICIP); 27 Octoer, p Keserci B, Yoshid H. Computerized detection of pulmonry nodules in chest rdiogrphs sed on morphologicl fetures nd wvelet snke model. Med Imge Anl 2002;6: Choi WJ, Choi TS. Computer Aided Detection of Pulmonry Nodules using Genetic Progrmming. In: Imge Processing (ICIP), th IEEE Interntionl Conference on 26 Septemer, p Suzuki K, Armto SG 3 rd, Li F, Sone S, Doi K. Mssive trining rtificil neurl network (MTANN) for reduction of flse positives in computerized detection of lung nodules in low dose computed tomogrphy. Med Phys 2003;30: Go Y, Lv Q, Feng Q, Chen WF. A New Method for Detection of Pulmonry Nodules. In: Bioinformtics nd Biomedicl Engineering, 2007: ICBBE; The 1 st Interntionl Conference on 6 July, p Yognnd BS, Mohn HS, Shivkumr G. Computer Aided Dignosis for the Detection of Lung Nodules. IOSR J Eng 2012;2: Nmin ST, Moghddm HA, Jfri R, Esmeil Zdeh M, Gity M. Automted Detection nd Clssifiction of Pulmonry Nodules in 3D Thorcic CT Imges. In: Systems Mn nd Cyernetics (SMC), 2010 IEEE Interntionl Conference on 10 Octoer, p Lin JS, Lo SB, Hsegw A, Freedmn MT, Mun SK. Reduction of flse positives in lung nodule detection using two level neurl clssifiction. IEEE Trns Med Imging 1996;15: Shi Z, Suzuki K, He L. Reducing Fps in Nodule Detection using Neurl Networks Ensemle. In: Informtion Science nd Engineering (ISISE), 2009 Second Interntionl Symposium on 26 Decemer, p Dolejsi M, Kyic J, Tum S, Polovinc KM. Reducing Flse Positive Responses in Lung Nodule Detector System y Asymmetric Adoost. In: Biomedicl Imging: From Nno to Mcro, ISBI th IEEE Interntionl Symposium on 14 My, p Guo W, Wei Y, Zhou H, Xue D. An Adptive Lung Nodule Detection Algorithm. In: Control nd Decision Conference, CCDC 09. Chinese 17 June, p Assef M, Fye I, Mlik AS, Shoi M. Lung Nodule Detection using Multi Resolution Anlysis. In: Complex Medicl Engineering (CME), 2013 ICME Interntionl Conference on 25 My, p Frg AA, Grhm J, Elshzly S, Frg A. Dt Driven Lung Nodule Models for Roust Nodule Detection in Chest CT. In: Pttern Recognition (ICPR), th Interntionl Conference on 23 August, p de Crvlho Filho AO, Silv AC, de Piv AC, Nunes RA, Gttss M. Lung nodule clssifiction sed on computed tomogrphy using txonomic diversity indexes nd n SVM. J Signl Process Syst 2017;87: Kuot T, Jereko AK, Dewn M, Slgnicoff M, Krishnn A. Segmenttion of pulmonry nodules of vrious densities with morphologicl pproches nd convexity models. Med Imge Anl 2011;15: Messy T, Hrdie RC, Tuinstr TR. Segmenttion of pulmonry nodules in computed tomogrphy using regression neurl network pproch nd its ppliction to the lung imge dtse consortium nd imge dtse resource inititive dtset. Med Imge Anl 2015;22: Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer

10 Ndelin, et l.: Detection of pulmonry nodules 19. Rudynto RD, Kerkstr S, vn Rikxoort EM, Fetit C, Brillet PY, Lefevre C, et l. Compring lgorithms for utomted vessel segmenttion in computed tomogrphy scns of the lung: The VESSEL12 study. Med Imge Anl 2014;18: Qin Y, Guirong W. Lung Nodule Segmenttion using EM Algorithm. Vol. 1. In: Intelligent Humn Mchine Systems nd Cyernetics (IHMSC), 2014 Sixth Interntionl Conference on 26 August, p Sun SS, Li H, Hou XR, Kng Y, Zho H. Automtic Segmenttion of Pulmonry Nodules in CT Imges. In: Bioinformtics nd Biomedicl Engineering, ICBBE The 1 st Interntionl Conference on 6 July, p Zinovev O, Zinovev D, Sien SA, Ricu DS, Furst J, Armto SG. A Texture Bsed Proilistic Approch for Lung Nodule Segmenttion. In: Interntionl Conference Imge Anlysis nd Recognition 22 June, Berlin, Heidelerg: Springer; p Okd K, Akdemir U. Blo Segmenttion using Joint Spce Intensity Likelihood Rtio Test: Appliction to 3D Tumor Segmenttion. In: Computer Vision nd Pttern Recognition, CVPR 2005, IEEE Computer Society Conference on 20 June, p Frg A, Grhm J, Frg A. Roust Segmenttion of Lung Tissue in Chest CT Scnning. In: Imge Processing (ICIP), th IEEE Interntionl Conference on 26 Septemer, p Chn TF, Vese LA. Active contours without edges. IEEE Trns Imge Process 2001;10: Kss M, Witkin A, Terzopoulos D. Snkes: Active contour models. Int J Comput Vis 1988;1: Lnkton S, Tnnenum A. Loclizing region sed ctive contours. IEEE Trns Imge Process 2008;17: ELCAP Pulic Lung Imge Dtse. Aville from: vi.cornell.edu/lungd.html. [Lst ccessed on 2017 Sep 06]. 29. Orozco HM, Villegs OO, Mynez LO, Snchez VG, Domnguez HD. Lung Nodule Clssifiction in Frequency Domin using Support Vector Mchines. In: Informtion Science, Signl Processing nd Their Applictions (ISSPA), th Interntionl Conference on 02 July, p Journl of Medicl Signls & Sensors Volume 7 Issue 4 Octoer-Decemer 2017