Semantics and image content integration for pulmonary nodule interpretation in thoracic computed tomography

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1 Semantcs and mage content ntegraton for pulmonary nodule nterpretaton n thoracc computed tomography Danela S. Racu a, Ekarn Varutbangkul a, Jane G. Csneros a, Jacob D. Furst a, Davd S. Channn b, Samuel G. Armato III c a Intellgent Multmeda Processng Laboratory, School of Computer Scence, Telecommuncatons, and Informaton Systems, DePaul Unversty, Chcago, IL 60604, USA; b Department of Radology, Northwestern Unversty Medcal School, Chcago, IL 60611, USA; c Department of Radology, The Unversty of Chcago, Chcago, IL 60637, USA ABSTRACT Useful dagnoss of lung lesons n computed tomography (CT) depends on many factors ncludng the ablty of radologsts to detect and correctly nterpret the lesons. Computer-aded Dagnoss (CAD) systems can be used to ncrease the accuracy of radologsts n ths task. CAD systems are, however, traned aganst ground truth and the mechansms employed by the CAD algorthms may be dstnctly dfferent from the vsual percepton and analyss tasks of the radologst. In ths paper, we present a framework for fndng the mappngs between human descrptons and characterstcs and computed mage features. The data n our study were generated from 9 thoracc CT scans collected by the Lung Image Database Consortum (LIDC). Every case was annotated by up to 4 radologsts by markng the contour of nodules and assgnng nne semantc terms to each dentfed nodule; ffty-nne mage features were extracted from each segmented nodule. Correlaton analyss and stepwse multple regresson were appled to fnd correlatons among semantc characterstcs and mage features and to generate predcton models for each characterstc based on mage features. From our prelmnary expermental results, we found hgh correlatons between dfferent semantc terms (margn, texture), and promsng mappngs from mage features to certan semantc terms (texture, lobulaton, spculaton, malgnancy). Whle the framework s presented wth respect to the nterpretaton of pulmonary nodules n CT mages, t can be easly extended to fnd mappngs for other modaltes n other anatomcal structures and for other mage features. Keywords: LIDC, shape, texture, stepwse multple regresson, correlaton analyss 1. INTRODUCTION Research studes have shown that double readng by two or more radologsts mproves the detecton of lung cancers, and the same have shown that nterpretaton performance vares greatly among radologsts. 1 Computer-aded dagnoss (CAD) systems can be used as a second reader to mprove the overall accuracy of radologsts n ths task. Snce the CAD algorthms typcally operate very dfferently from human percepton t can be dffcult at tmes to understand how certan fndngs were made. The purpose of ths research s to provde a quanttatve approach for fndng the relatonshps between mage features and medcal terms (semantc concepts) used for dagnoss and fndng mappngs from mage features to semantc terms. Our approach can assst radologsts n nterpretng lung nodules by provdng ether an ntal or secondary estmate of the semantc values based on the calculaton and subsequent analyss of mage features. Ths can serve to ncrease the accuracy of a sngle reader and mprove the consstency among multple readers.. RELATED WORK Several research studes have desgned CAD systems that can help estmate the probablty of cancer based on nodule characterstcs (such as nodule sze, shape, and nternal structures) and clncal nformaton (such as age, gender, hstory of smokng, and hstory of cancer).

2 McNtt-Gray et al., 3 used nodule sze, shape and co-occurrence texture features as nodule characterstcs to desgn a lnear dscrmnant analyss classfcaton system for malgnant versus bengn nodules. Lo et al. 4 used drecton of vascularty, shape and nternal structure to buld an artfcal neural network classfcaton system for predcton of the malgnancy of the nodules. Armato and MacMahon 5 used nodule appearance and shape to buld a LDA classfcaton system of pulmonary nodules n malgnant versus bengn classes. Takashma et al. 6, 7 used shape nformaton to characterze malgnant versus bengn lesons n the lung. Whle these systems are based on just nodule characterstcs, there are also studes that make use of clncal nformaton. J. Gurney desgned a Bayesan classfcaton system 8, 9 based on clncal nformaton n addton to radologcal nformaton. Y. Matsuk et al. 10 also used clncal nformaton n addton to sxteen features scored by radologsts to desgn an ANN for malgnancy versus bengn classfcaton. M. Aoyama et al. 11 used two clncal features n addton to forty-one mage features to determnaton of the lkelhood measure of malgnancy for pulmonary nodules on low-dose CT mages. For a more detaled lterature revew on the analyss of computed tomography scans of the lung, we recommend the recent survey by I. Slumer et al. 1 All these studes are performed on dfferent data sets and therefore, a comparson of the algorthms presented by these studes s not relable. The necessty of creatng benchmark data sets has been recognzed and the Lung Image Database Consortum 13 (LIDC) benchmark s a frst step undertaken n ths drecton. The LIDC has been already used for the valdaton of two detecton algorthms. Usng the LIDC cases, Lu and L 14 also proposed a new method for nodule detecton based on gradent and ntensty combned level set methods that generated stable and accurate segmentaton results for complex organc structures lke lung broncha and nodules. All studes used a combnaton of features to characterze the sze, shape and nternal structure of the nodules. In that way, they ndrectly encoded radologsts knowledge about ndcators of malgnancy 1. However, the real queston s f ndeed the nodule characterstcs as encoded by the CAD systems correspond to the same concepts as the ones used by the radologsts. In other words, t s mportant to have a correct mappng between the nodule mage features and the semantc concept used by radologst to annotate the nodule. For example, can we gve a postve answer to the queston: are the computerzed texture features quantfyng the texture as perceved by radologsts? In other words, s computed texture the same as the perceved texture? Furthermore, s the texture of a nodule perceved the same by two dfferent radologsts? It was shown that when human ratngs of nodule characterstcs are used to tran computer systems, such ratngs are not always relable and reproducble Moreover, n practce, physcans use several perceptual categores to make dagnoses. Frst steps have already been taken n creatng frameworks for a common language when makng dagnoss. Leroy and Chen 19 developed a tool (Medcal Concept Mapper) based on the Unfed Medcal Language System 0 (UMLS) and WordNet 1 that connects patent nformaton to human-created ontologes. Barb et al. proposed a framework that uses semantc methods to descrbe vsual abnormaltes and exchange knowledge n the medcal doman. Our work can also be consdered one of the ntal steps n the drecton of mappng mage features to perceptual categores encodng the radologsts knowledge for lung nterpretaton. 3. METHODOLOGY In ths secton we present our proposed methodology for fndng the mappngs between the nodule mage features and the physcan annotatons as summarzed n Fgure 1. In Secton 3.1 we present the data set. In Secton 3. we present the mage processng algorthms used to perform automatc nodule feature extracton and n Secton 3.3 we present the data analyss and machne learnng algorthms used to map the mage features to the physcans annotatons.

3 3.1 Data Set Fgure 1: Dagram of the proposed mappng framework The data used n ths study were generated from 9 cases of thoracc CT collected by the Lung Image Database Consortum (LIDC). 13 In the LIDC s markng process, up to 4 radologsts marked the boundary of lung nodules wth szes between 3 mm and 3 cm for every slce on whch the nodule appears and rated nne semantc characterstcs for each dentfed nodule. The nne semantc characterstcs chosen by the LIDC are shown n the Table 1, along wth our notes and references on the meanng of these characterstcs based on our lterature revew, and the range of possble scores from whch the ratng radologsts could choose. In the markng process, there s no forced agreement on the exstence of a nodule, ts locaton, ts boundary, or ts characterstcs 13. Table 1: LIDC nodule characterstcs wth correspondng lung nodules Characterstc Notes and References Possble Scores Calcfcaton calcfcaton appearance n the nodule - The smaller the nodule, 1. Popcorn the more lkely t must contan calcum n order to be. Lamnated vsualzed. 3 Bengnty s hghly assocated wth central, noncentral, 3. Sold lamnated, and popcorn calcfcaton. 4,5 4. Non-central 5. Central 6. Absent Internal structure expected nternal composton of the nodule 1. Soft Tssue. Flud 3. Fat 4. Ar 1. Marked Lobulaton whether a lobular shape s apparent from the margn or not - lobulated margn s an ndcaton of bengnty None Malgnancy lkelhood of malgnancy of the nodule - Malgnancy s assocated wth large nodule sze whle small nodules are more lkely to be bengn. 4,6 Most malgnant nodules are noncalcfed 3 and have spculated margns Hghly Unlkely. Moderately Unlkely 3. Indetermnate 4. Moderately Suspcous 5. Hghly Suspcous Margn how well defned the margns of the nodule are 1. Poorly Defned Sharp Sphercty dmensonal shape of nodule n terms of ts roundness 1. Lnear.. 3. Ovod Round

4 Spculaton Subtlety Texture degree to whch the nodule exhbts spcules, spke-lke structures, along ts border - Spculated margn s an ndcaton of malgnancy. 5,8 dffculty n detecton - Subtlety refers to the contrast between the lung nodule and ts surroundngs nternal densty of the nodule - Texture plays an mportant role when attemptng to segment a nodule, snce part-sold and nonsold texture can ncrease the dffculty of defnng the nodule boundary Marked None 1. Extremely Subtle. Moderately Subtle 3. Farly Subtle 4. Moderately Obvous 5. Obvous 1. Non-Sold.. 3. Part Sold/(Mxed) Sold 3. Feature extracton In order to quantfy the mage content, we calculated four types of mage features for each nodule: sze, shape, ntensty, and texture; ths feature extracton stage generated 59 mage features as presented n Table. The choce of these features was based on a lterature revew of the most common mage features used for pulmonary nodule detecton and dagnoss by exstent CAD systems 8, 9. Table : The entre set of mage features extracted from each segmented lung nodule. Shape Features Sze Features Intensty Features Texture Features Crcularty Area MnIntensty Roughness ConvexArea MaxIntensty Elongaton Permeter MeanIntensty Compactness ConvexPermeter SDIntensty Eccentrcty EquvDameter MnIntenstyBG Soldty MajorAxsLength MaxIntenstyBG Extent MnorAxsLength MeanIntenstyBG RadalDstanceSD SDIntenstyBG IntenstyDfference 11 Haralck features calculated from co-occurrence matrces (Contrast, Correlaton, Entropy, Energy, Homogenety, 3 rd Order Moment, Inverse Dfferental Moment, Varance, Sum Average, Cluster Tendency, Maxmum Probablty) 4 Gabor features - mean and standard devaton of Gabor flters consstency of four orentatons and three scales. In order to quantfy the shape of a nodule, we used eght common mage shape features: crcularty, roughness, elongaton, compactness, eccentrcty, soldty, extent, and the standard devaton of the radal dstance. Crcularty s measured by dvdng the area of the regon by the area of a crcle wth the same convex permeter. Roughness can be measured by dvdng the permeter of the regon by the convex permeter. A smooth convex object, such as a perfect crcle, wll have a roughness of 1.0. The eccentrcty s obtaned usng the ellpse that has the same second-moments as the regon. The eccentrcty s the rato of the dstance between the foc of the ellpse and ts major axs length. The value s between 0 (a perfect crcle) and 1 (a lne). Soldty s defned n terms of the convex hull correspondng to the regon beng the proporton of the pxels n the convex hull that are also n the regon. Extent s the proporton of the pxels n the boundng box (the smallest rectangle contanng the regon) that are also n the regon. Fnally, the RadalDstanceSD s the standard devaton of the dstances from every boundary pxel to the centrod of the regon. For the sze of a nodule, the followng seven features were found to be the most common ones: area, convexarea, permeter, convexpermeter, equvdameter, majoraxslength, mnoraxslength. The area and permeter mage features measure the actual number of pxels n the regon and on the boundary, respectvely. The convexarea and convexpermeter measure the number of pxels n the convex hull and on the boundary of the convex hull correspondng to the nodule regon. EquvDameter s the dameter of a crcle wth the same area as the regon. Lastly, the majoraxslength and mnoraxslength gve the length (n pxels) of the major and mnor axes of the ellpse that has the same normalzed second central moments as the regon. Gray-level ntensty features used n ths study are smply the mnmum, maxmum, mean, and standard devaton of the gray-level ntensty of every pxel n each segmented nodule mage and the same four values for every background pxel n the boundng box contanng each segmented nodule mage. Another feature, ntenstydfference, s the absolute value

5 of the dfference between the mean of the gray-level ntensty of the segmented nodule mage and the mean of the graylevel ntensty of ts background. Normally texture analyss can be grouped nto four categores: model-based, statstcal-based, structural-based, and transform-based methods. Structural approaches seek to understand the herarchal structure of the mage, whle statstcal methods descrbe the mage usng pure numercal analyss of pxel ntensty values. Transform approaches generally perform some knd of modfcaton to the mage, obtanng a new response mage that s then analyzed as a representatve proxy for the orgnal mage, and model-based methods are based on the concept of predctng pxel values based on a mathematcal model. Based on our prevous texture analyss work 31, n ths research we focus on two well-known texture analyss technques: co-occurrence matrces (a statstcal-based method), and Gabor flters (a transform-based method). Co-occurrence matrces focus on the dstrbutons and relatonshps of the gray-level ntensty of pxels n the mage. They are calculated along four drectons (0º, 45º, 90º, and 135º) and fve dstances (1,, 3, 4 and 5 pxels) producng 0 co-occurrence matrces. Once the co-occurrence matrces are calculated, eleven Haralck texture descrptors 31 are then calculated from each co-occurrence matrx. Although each Haralck texture descrptor s calculated from each cooccurrence matrx, we averaged the features by dstance and then select the mnmum value by drecton resultng n 11 (nstead of 11*4*5) Haralck features per mage. Gabor flterng 3 s a transform based method whch extracts texture nformaton from an mage n the form of a response mage. A Gabor flter s a snusod functon modulated by a Gaussan and dscretzed over orentaton and frequency. We convolve the mage wth 1 Gabor flters: four orentatons (0º, 45º, 90º, and 135º) and three frequences (0.3, 0.4, and 0.5), where frequency s the nverse of wavelength. The sze of each Gabor flter s set constant at9 9. Our Gabor flter desgn s based on the work by Andrysak and Choras, 3 where Gabor flters were used to encode the mage content for mage retreval. We then calculate means and standard devatons from the 1 response mages resultng n 4 Gabor features per mage. At the end of the mage feature extracton process, each nodule mage s encoded usng a set of ffty-nne mage features f, = 1, 59 and nne radologst annotatons c j, j = 1, 9 (semantc concepts). Therefore, the nodule representaton s gven by the vector representaton[ f 1, f, f59, c1, c, c9 ]; Fgure shows an example of feature values for a nodule representaton. We should note here that each nodule mght produce up to four mages n the data set gven that same nodule can be delneated dfferently by the four radologsts. Furthermore, snce the same nodule can be rated dfferently by the four radologsts, for the same nodule mage there mght be up to four vector representatons each representaton encodng the ratng of one of the radologsts. Fgure : An example of nodule characterstcs assgned by a radologst and features extracted from the segmented nodule.

6 3.3 Automatc Mappngs Extracton In the mage ndexng and retreval communty, the lack of concdence between the nformaton that one can extract from the vsual data and the nterpretaton that the same data has for a user n a gven stuaton s known as the semantc gap problem. A look on the state-of-the-art n the mage ndexng and retreval doman shows the mportance and the complexty of the semantc problem. In ths paper, we address ths problem as encountered n the lung nodule mage nterpretaton by proposng two basc statstcal technques to dentfy the relatonshps between the dgtal representaton of a nodule mage and ts nterpretaton as perceved by up to four radologsts. Because the radologsts were asked to provde quantfed semantc descrptons, ths data set lends tself well to basc statstcal methods. Frst, we chose to concentrate on correlatons among all pars of semantc ratngs as well as all (semantc ratng, mage feature) pars. Usng the nodule vector representaton ntroduced n the prevous secton, the Spearman s rank correlaton coeffcent 34 among two semantc ratngs c, c j s defned as follows: ρ c cj k= 1,... n, = 1,, j = 1 ( c cj) 6 ( r r ) nn ( 1) 9 (1) where n stands for the number of data ponts (nodule mages),, r are the ranks of the semantc ratngs, respectvely. Smlarly, the correlaton among a semantc ratng c and an mage feature s defned as follows: where rf s the rank of the mage feature j 6 ( rc r ) fj k= 1,... n ρ ( c, f j) = 1, = 1 9, j = 1 59 () nn ( 1) f j. Values of the correlaton coeffcent r c c j f j c, c ρ close to 1 wll ndcate a postve strong correlaton between the consdered pars. Further, the par-wse results for basc correlatons can be used to compare our results to what has been reported n prevous studes regardng the relatonshp between lung nodule semantc descrptons and physcal descrptons of the nodule. However, the correlaton models can only account for par-wse groupngs of semantc ratngs and thus provde lmted predctve power. To nvestgate further the nteracton of all mages features and ther combned relatonshp to the semantc ratngs, we used a stepwse multple regresson. Stepwse multple regresson analyss 33 was appled to generate predcton models M for each characterstc c based on all mage features : f j M : (3) c = β 0 + β f k + ε k = 1, where p s the number of mage features used n the regresson model, M k p β are the regresson coeffcents, and ε are the adj _ R predcton errors per model. A model was consdered to be a good ft f the (the square of the adjusted correlaton coeffcent) was greater than 0.8 whch mples that the model captured more than 80% of the varance n the data: ( ) ( n 1) adj _ R = 1 1 R (4) n p 1 ( ) The adjusted value of the correlaton coeffcent or more mage features are dependent). Unlke the R, the adj _ R mprove the regresson model. R was used to take nto account the mult-colnearty n the data (two can declne f the addton of varable does not j

7 Besdes modelng each semantc term or characterstc wth respect to only the mage features and thus ncludng all the nodule mages n the analyss, we also looked nto the modelng of the relatonshps among the features and semantc terms when: 1) at least radologsts and ) at least 3 radologsts agreed on the same ratng for the characterstc to be predcted. The adj_r calculated for each characterstc was also used to measure the mprovement n the predcted multple regresson models when there was better agreement among the radologsts. The stepwse feature selecton ncorporated nto the regresson model also provded a way to measure the contrbuton of each of the selected mage features to predct the correspondng semantc characterstc. Furthermore, the lnear combnaton of the most mportant features can be used to quantfy the human percepton of the correspondng vsual characterstcs as perceved by the radologsts and thus allows the ntegraton of semantcs and low-level mage content. 4. RESULTS From our experments, we found many nterestng correlatons among nodule characterstcs and mage features, and statstcally sgnfcant predcton models were obtaned for several nodule characterstcs. 4.1 Correlaton analyss For ths work, we calculated 4 correlaton coeffcents for comparng semantc ratngs. The data set for each ratng was smply the set of scores that all radologsts provded for all nodules; the calcfcaton and nternal structure ratngs were not ncluded n the correlaton analyss snce they have categorcal values. Snce all ratngs appear together for each nodule, the correspondence between features s trval. We also calculated 413 correlaton coeffcents for each combnaton of semantc ratng and mage feature. Whle the number of coeffcents was large, ths knd of a basc statstcal analyss gves us a very good pcture of relatonshps between semantc concepts and between concepts and mage features. Gven the varablty n the radologst ratngs and the complexty of such an annotaton process, we consdered as meanngful assocatons even those assocatons that produced correlaton values greater than 0.3. As shown n Fgure 3, we found that: 1) Subtlety and malgnancy (as assessed by the radologsts) are correlated wth each other and both of them are correlated wth many sze features (Area, ConvexArea, Permeter, ConvexPermeter, EquvDameter, MajorAxsLength, and MnorAxsLength). These fndngs were also supported by the lterature revew that malgnancy s assocated wth the nodule sze. 5,7 ) Sphercty s correlated wth some shape features that are related to roundness of the regon (Elongaton, Eccentrcty, Extent, and Crcularty) as expected. 3) Margn and texture are correlated wth each other and ths correlaton was n concordance wth other research studes showng that texture plays an mportant role n nodule segmentaton snce part-sold and non-sold texture can ncrease the dffculty of defnng the nodule boundary. 30 Furthermore, margn and texture are not drectly correlated wth any of the ndvdual mage features. Same pattern was notced for lobulaton and spculaton whch were correlated wth each other but not correlated wth any of the ndvdual mage features.

8 4. Stepwse multple regresson analyss Fgure 3: Correlatons among nodule characterstcs and mage features The adj_r values of all regresson models for all characterstcs are presented n Table 3. The second column ndcates the results on all nodules n the data set, whle the thrd column shows results only for mages on whch at least two radologsts agreed on a ratng for that nodule. The fourth column shows smlar results for nodules on whch at least three radologsts agreed on a ratng. From the stepwse multple regresson analyss, we learned that the mage features collected for ths study can be used to produce good predcton models for malgnancy, lobulaton, texture, and spculaton ( adj _ R values of 0.990, 0.877, 0.843, and respectvely); all the models were statstcally sgnfcant ( α = 0.05 ) as well. As we expected, the predcton models mproved as the agreement among the radologsts mproved; for example, the adj _ R went up from.310 to when nstead of consderng all nodules we consdered the ones on whch at least two radologsts agreed on malgnancy and further went up to when at least three radologsts agreed for the same feature. The predcton models for the nodule data on whch at least three radologsts agreed are presented n Fgures 4, 5, 6, and 7. The regresson coeffcents and ther correspondng p-values show the contrbuton and sgnfcance of each feature to the regresson model; the large values for the F-tests show a strong support for the found lnear regresson models. The mage features that show up n the models are the most mportant features and they are selected f the p- values for the tests showng ther contrbutons to the model are less than Table 3: Adj_R of the stepwse multple regresson models of each characterstc; n all cases, the number of mages and the number of nodules ncluded n the dataset are shown n parentheses. The largest adj_r values; a cell for whch there s a dash ndcates that radologst agreement occurred only for a sngle ratng. Characterstcs Entre dataset At least radologsts agreed At least 3 radologsts agreed (1106 mages, 73 nodules) Calcfcaton (884, 41) (644, 1) Internal Structure (855, 40) - (659, ) Lobulaton (448, 4) (137, 6) Malgnancy (489, 3) (107, 5) Margn (519, 8) - (45, 7) Sphercty (575, 7) 0.68 (07, 9) Spculaton (61, 9) (8, 9) Subtlety (659, 5) (360, 10) Texture (736, 33) (437, 15)

9 Characterstcs Calcfcaton InternalStructure Lobulaton Malgnancy Margn Sphercty Spculaton Adj_R = F-value = p-value = Regresson Coeffcents p-value (Constant) E-54 Gabormean_45º_ E-07 MnIntenstyBG E-8 Energy E-1 Gabormean_0º_ E-14 IntestyDfference InverseVarance E-05 Gabormean_45º_ Gabormean_90º_ E-05 Correlaton E-05 ClusterTendency 5.16E ConvexPermeter Subtlety Texture Estmated Malgnancy = Gabormean_45º_ MnIntenstyBG Energy Gabormean_0º_ IntestyDfference InverseVarance Gabormean_45º_ Gabormean_90º_ Correlaton E-06 ClusterTendency ConvexPermeter Fgure 4: A predcton model for malgnancy. Fgure 5: A predcton model for spculaton

10 Fgure 6: A predcton model for texture Fgure 7: A predcton model for lobulaton

11 5. CONCLUSIONS In the past, researchers have developed several CAD systems for the detecton and classfcaton of pulmonary nodules. Most of these systems mmc doman knowledge n order to extract mage content and they use a comparson wth ground truth for dagnoss. They do so, however, n an algorthmc fashon that s only tenuously related to human percepton and characterzaton of mage features. Through the use of stepwse multple regresson, we proposed a quanttatve model for fndng the mappngs between these two types of nformaton. We found that the radologsts percepton wth respect to lobulaton, spculaton, texture and malgnancy s captured wth hgh accuracy based on the low-level mage features used n ths study. Our prelmnary results are promsng and can be consdered the foundaton of buldng computerzed systems for detecton, dagnoss, and medcal mage retreval usng radologst-defned semantcs. Although the approach s presented for the lung nodules, t can be easly extended to other modaltes, anatomcal structures and mage features. Furthermore, we also provde an approach for fndng the correlatons among dfferent semantc concepts used to descrbe the same vsual pattern; n the long term, ths approach can help create a doman-specfc ontology of mage feature descrptors. REFERENCES 1. J. Burns, L. B. Haramat, K. Whtney, M. N. Zelefsky, Consstency of Reportng Basc Characterstcs of Lung Nodules and Masses on Computed Tomography, Academc Radology, vol. 11, 3-37, M. F. McNtt-Gray, E.M. Hart, N.Wyckoff, J.W. Sayre, J. G. Goldn, and D. R. Aberle, A pattern classfcaton approach to characterzng soltary pulmonary nodules maged on hgh resoluton CT: Prelmnary results, Medcal Physcs, vol. 6, no. 6, pp , M. F. McNtt-Gray, N.Wyckoff, J.W. Sayre, J. G. Goldn, and D. R. Aberle, The effects of co-occurrence matrx based texture parameters on the classfcaton of soltary pulmonary nodules maged on computed tomography, Computerzed Medcal Imagng and Graphcs, vol. 3, no. 6, pp , S.-C. B. Lo, M. T. F. L-Yueh Hsu and, Y.-M. F. Lure, and H. Zhao, Classfcaton of lung nodules n dagnostc CT: An approach based on 3-D vascular features, nodule densty dstrbutons, and shape features, n Proceedngs of the SPIE, vol. 503, 003, pp Armato SG III, Altman MB, Wlke J, Sone S, L F, Do K, Roy AS: Automated lung nodule classfcaton followng automated nodule detecton on CT: A seral approach. Medcal Physcs 30: , S. Takashma, S. Sone, F. L, Y. Maruyama, M. Hasegawa, and M. Kadoya, Indetermnate soltary pulmonary nodules revealed at populaton-based CT screenng of the lung: usng frst follow-up dagnostc CT to dfferentate bengn and malgnant lesons, Amercan Journal of Roentgenology, vol. 180, no. 5, pp , S. Takashma, S. Sone, F. L, Y. Maruyama, M. Hasegawa, T. Matsushta, F. Takayama, and M. Kadoya, Small soltary pulmonary nodules ( 1 cm) detected at populaton-based CT screenng for lung cancer: relable hghresoluton CT features of bengn lesons, Amercan Journal of Roentgenology, vol. 180, no. 4, pp , J. Gurney, Determnng the lkelhood of malgnancy n soltary pulmonary nodules wth Bayesan analyss. Part I. Theory, Radology, vol. 186, no., pp , J. Gurney, D. Lyddon, and J. McKay, Determnng the lkelhood of malgnancy n soltary pulmonary nodules wth Bayesan analyss. Part II. Applcaton, Radology, vol. 186, no., pp , Y. Matsuk, K. Nakamura, H. Watanabe, T. Aok, H. Nakata, S. Katsuragawa, and K. Do, Usefulness of an artfcal neural network for dfferentatng bengn from malgnant pulmonary nodules on hgh-resoluton CT: Evaluaton wth recever operatng characterstc analyss, Amercan Journal of Roentgenology, vol. 178, no. 3, pp , M. Aoyama, Q. L, S. Katsuragawa, F. L, S. Sone, and K. Do, Computerzed scheme for determnaton of the lkelhood measure of malgnancy for pulmonary nodules on low-dose CT mages, Medcal Physcs, vol. 30, no. 3, pp , I. Slumer, A. Schlham, M. Prokop, and B. Gnneken, Computer Analyss of Computed Tomography Scans of the Lung: A Survey, IEEE Transactons on Medcal Imagng, vol. 5, No. 4, S. G. Armato, G. McLennan, M. F. McNtt-Gray, C. R.Meyer, D. Yankelevtz, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Kazeroon, H. MacMahon, A. P. Reeves, B. Y. Croft, and L. P. Clarke, Lung Image Database Consortum: Developng a resource for the medcal magng research communty, Radology, vol. 3, no. 3, pp , 004.

12 14. S. Lu, and J. L, Automatc Medcal Image Segmentaton Usng Gradent and Intensty Combned Level Set Method, The 8th IEEE EMBS Annual Internatonal Conference, , New York Cty, G. D. Rubn, John K. Lyo, D.S. Pak, A. J. Sherbondy et al. Pulmonary Nodules on Mult Detector Row CT Scans: Performance Comparson of Radologsts and Computer-aded Detecton, Radology 005;34: F. L, Q. L, R. Engelmann, M. Aoyama, S. Sone, H. MacMahon, K. Do, Improvng Radologsts Recommendatons Wth Computer-Aded Dagnoss for Management of Small Nodules Detected by CT. Academc Radology, Volume 13, Issue 8, Pages R Wemker, P Rogalla, T Blaffert, D Sfr, O Hay, et al. Aspects of computer-aded detecton (CAD) and volumetry of pulmonary nodules usng multslce CT Brtsh Journal of Radology (005) 78, S46-S J. Burns, L. B. Haramat, K. Whtney, and M. N. Zelefsky, Consstency of reportng basc characterstcs of lung nodules and masses on computed tomography, Academc Radology, vol. 11, pp , G. Leroy and H. Chen, Meetng medcal termnology needs-the ontology enhanced medcal concept mapper, IEEE Transacton on Informaton Technology n Bomedcne, vol. 5, no 4, D. A. B. Lndberg and B. L. Humphreys, UMLS Knowledge Sources, 14 th ed. Bethesda, MD: Na. Lbrary Med., AB. vol. 5, no. 4, pp , Dec G. A. Mller, WordNet: A lexcal database for Englsh, Communcatons ACM, vol. 38, no. 11, pp , A.S. Barb, C-R Shyu, and Y. P. Seth, Knowledge Representaton and Sharng Usng Vsual Semantc Modelng for Dagnostc Medcal Image Databases, IEEE Transactons Informaton Technology n Bomedcne, vol. 9, no.4, L. Keta, M. Malby, K. Jordan, A. Meholc, J. Locken, Small Nodules Detected on Chest Radography: Does Sze Predct Calcfcaton? CHEST, vol. 118, , N. Hollngs, P. Shaw, Dagnostc Imagng of Lung Cancer, European Respratory Journal, vol. 19, 7-74, E. A. Zerhoun, F. P. Sttk, S. S. Segelman, D. P. Nadch, S. S. Sagel et al. CT of the Pulmonary Nodule: A Cooperatve Study, Radology, vol. 160, , S. S. Segelman, N. F. Khour, F. P. Leo, E. K. Fshman, R. M. Braverman, E. A. Zerhoun, Soltary Pulmonary Nodules: CT Assessment, Radology, vol. 160, , S. Matsuoka, Y. Kurhara, K. Yaghash, H, Nm, Y. Nakajma, Perpheral Soltary Pulmonary Nodule: CT Fndngs n Patents wth Pulmonary Emphysema, Radology, vol. 35, 66-73, B. Zhao, G. Gamsu, M. S. Gnsberg, L. Jang, L. H. Schwartz, Automatc Detecton of Small Lung Nodules on CT Utlzng a Local Densty Maxmum Algorthm, Journal of Appled Clncal Medcal Physcs, vol. 4, Issue 3, 48-60, J. M. Goo, J. W. Lee, H. J. Lee, S. Km, J. H. Km, J. Im, Automated Lung Nodule Detecton at Low-Dose CT: Prelmnary Experence, Korean J Radology, vol. 4, 11-16, J.D. Furst, R. Susomboon, and D.S. Racu, "Sngle Organ Segmentaton Flters for Multple Organ Segmentaton", IEEE 006 Internatonal Conference of the Engneerng n Medcne and Bology Socety (EMBS'06), August R. M. Haralck, K. Shanmugam, and I. Dnsten, Textural Features for Image Classfcaton, IEEE Trans. On Systems, Man, and Cybernetcs, vol. 3, no. 6, , T. Andrysak and M. Choras, Image retreval based on herarchcal Gabor flters, Internatonal Journal Appled Computer Scence, vol. 15, no. 4, , R. L. Ott and M.T. Longnecker, An Introducton to Statstcal Methods and Data Analyss, Duxbury Press (000), 5th edton. 34. C. Spearman, The proof and measurement of assocaton between two thngs, Amercan Journal of Psychology, vol. 15, 7-101, 1904.

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