View Item 

Show simple item record

Computerâ aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours
dc.contributor.authorWay, Ted W.
dc.contributor.authorHadjiiski, Lubomir M.
dc.contributor.authorSahiner, Berkman
dc.contributor.authorChan, Heang‐ping
dc.contributor.authorCascade, Philip N.
dc.contributor.authorKazerooni, Ella A.
dc.contributor.authorBogot, Naama
dc.contributor.authorZhou, Chuan
dc.date.accessioned2017-01-06T20:49:50Z
dc.date.available2017-01-06T20:49:50Z
dc.date.issued2006-07
dc.identifier.citationWay, Ted W.; Hadjiiski, Lubomir M.; Sahiner, Berkman; Chan, Heang‐ping ; Cascade, Philip N.; Kazerooni, Ella A.; Bogot, Naama; Zhou, Chuan (2006). "Computerâ aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours." Medical Physics 33(7): 2323-2337.
dc.identifier.issn0094-2405
dc.identifier.issn2473-4209
dc.identifier.urihttps://hdl.handle.net/2027.42/135040
dc.publisherAmerican Association of Physicists in Medicine
dc.publisherWiley Periodicals, Inc.
dc.subject.othermedical image processing
dc.subject.otherfeature extraction
dc.subject.otheroptimisation
dc.subject.othercomputerâ aided diagnosis
dc.subject.otheractive contour model
dc.subject.otherobject segmentation
dc.subject.otherclassification
dc.subject.othertexture analysis
dc.subject.othercomputed tomography (CT)
dc.subject.othermalignancy
dc.subject.otherpulmonary nodule
dc.subject.otherLungs
dc.subject.otherCancer
dc.subject.otherRadiologists
dc.subject.otherComputed tomography
dc.subject.otherMedical imaging
dc.subject.otherComputer aided diagnosis
dc.subject.otherMedical image segmentation
dc.subject.otherCluster analysis
dc.subject.otherDatabases
dc.subject.otherTesting procedures
dc.subject.othertumours
dc.subject.otherComputed radiography
dc.subject.otherImage analysis
dc.subject.otherDiseases
dc.subject.otherNumerical optimization
dc.subject.othercomputerised tomography
dc.subject.otherdiagnostic radiography
dc.subject.otherlung
dc.subject.othercancer
dc.subject.otherimage classification
dc.subject.otherimage segmentation
dc.titleComputerâ aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, Michigan 48109
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/135040/1/mp7129.pdf
dc.identifier.doi10.1118/1.2207129
dc.identifier.sourceMedical Physics
dc.identifier.citedreferenceK. Suzuki, F. Li, S. Sone, and K. Doi, â Computerâ aided diagnostic scheme for distinction between benign and malignant nodules in thoracic lowâ dose CT by use of massive training artificial neural network,â IEEE Trans. Med. Imaging ITMID4 --> 0278‐0062 --> 24, 1138 â 1150 ( 2005 ).
dc.identifier.citedreferenceS. Diederich, D. Wormanns, M. Semik, M. Thomas, H. Lenzen, N. Roos, and W. Heindel, â Screening for early lung cancer with lowâ dose spiral CT: Prevalence in 817 asymptomatic smokers,â Radiology RADLAX --> 0033‐8419 --> 222, 773 â 781 ( 2002 ).
dc.identifier.citedreferenceM. Kaneko, K. Eguchi, H. Ohmatsu, R. Kakinuma, T. Naruke, K. Suemasu, and N. Moriyama, â Peripheral lung cancer: Screening and detection with lowâ dose spiral CT versus radiography,â Radiology RADLAX --> 0033‐8419 --> 201, 798 â 802 ( 1996 ).
dc.identifier.citedreferenceT. Nawa, T. Nakagawa, S. Kusano, Y. Kawasaki, Y. Sugawara, and H. Nakata, â Lung cancer screening using lowâ dose spiral CT: Results of baseline and 1â year followâ up studies,â Chest CHETBF --> 0012‐3692 --> 122, 15 â 20 ( 2002 ).
dc.identifier.citedreferenceS. Sone, S. Takashima, F. Li, F. Yang, T. Honda, Y. Maruyama, M. Hasega, T. Yamanda, K. Kubo, K. Hanamura et al., â Mass screening for lung cancer with mobile spiral computed tomography scanner,â Lancet LANCAO --> 0140‐6736 --> 10.1016/S0140â 6736(97)08229â 9 352, 1242 â 1245 ( 1998 ).
dc.identifier.citedreferenceS. Sone, F. Li, Z. G. Yang, T. Honda, Y. Maruyama, S. Takashima, M. Hasegawa, S. Kawakami, K. Kubo, K. M. Haniuda et al., â Results of threeâ year mass screening programme for lung cancer using mobile lowâ dose spiral computed tomography scanner,â Br. J. Cancer BJCAAI --> 0007‐0920 --> 10.1054/bjoc.2000.1531 84, 25 â 32 ( 2001 ).
dc.identifier.citedreferenceS. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, S. J. Mandrekar, S. L. Hillman, A.â M. Sykes, G. L. Aughenbaugh, and A. O. B. L. Allen, â CT screening for lung cancer: Fiveâ year prospective experience,â Radiology RADLAX --> 0033‐8419 --> 235, 259 â 265 ( 2005 ).
dc.identifier.citedreferenceS. J. Swensen, J. R. Jett, T. E. Hartman, D. E. Midthun, J. A. Sloan, A. M. Sykes, G. L. Aughenbaugh, and M. A. Clemens, â Lung cancer screening with CT: Mayo Clinic experience,â Radiology RADLAX --> 0033‐8419 --> 226, 756 â 761 ( 2003 ).
dc.identifier.citedreferenceT. W. Freer and M. J. Ulissey, â Screening mammography with computerâ aided detection: Prospective study of 12,860 patients in a community breast center,â Radiology RADLAX --> 0033‐8419 --> 220, 781 â 786 ( 2001 ).
dc.identifier.citedreferenceS. G. Armato, M. L. Giger, C. J. Moran, J. T. Blackburn, K. Doi, and H. MacMahon, â Computerized detection of pulmonary nodules on CT scans,â Radiographics 0271â 5333 19, 1303 â 1311 ( 1999 ).
dc.identifier.citedreferenceM. S. Brown, M. F. McNittâ Gray, J. G. Goldin, R. D. Suh, J. W. Sayre, and D. R. Aberle, â Patientâ specific models for lung nodule detection and surveillance in CT images,â IEEE Trans. Med. Imaging ITMID4 --> 0278‐0062 --> 20, 1242 â 1250 ( 2001 ).
dc.identifier.citedreferenceP. Croisille, M. Souto, M. Cova, S. Wood, Y. Afework, J. E. Kuhlman, and E. A. Zerhouni, â Pulmonary nodules: Improved detection with vascular segmentation and extraction with spiral CT,â Radiology RADLAX --> 0033‐8419 --> 197, 397 â 401 ( 1995 ).
dc.identifier.citedreferenceR. Malladi, J. A. Sethian, and B. C. Vemuri, â Shape modeling with front propagation: A level set approach,â IEEE Trans. Pattern Anal. Mach. Intell. ITPIDJ --> 0162‐8828 --> 10.1109/34.368173 17, 158 â 175 ( 1995 ).
dc.identifier.citedreferenceJ. Yang, L. H. Staib, and J. S. Duncan, â Neighborâ constrained segmentation with level set based 3â D deformable models,â IEEE Trans. Med. Imaging ITMID4 --> 0278‐0062 --> 23, 940 â 948 ( 2004 ).
dc.identifier.citedreferenceM. B. Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.â G. Villemure, and J.â P. Thiran, â Atlasâ based segmentation of pathological MR brain images using a model of lesion growth,â IEEE Trans. Med. Imaging ITMID4 --> 0278‐0062 --> 23, 1301 â 1314 ( 2004 ).
dc.identifier.citedreferenceM. N. Gurcan, B. Sahiner, N. Petrick, H. P. Chan, E. A. Kazerooni, P. N. Cascade, and L. Hadjiiski, â Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computerâ aided diagnosis system,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.1515762 29, 2552 â 2558 ( 2002 ).
dc.identifier.citedreferenceM. S. Brown, J. G. Goldin, R. D. Suh, M. F. McNittâ Gray, J. W. Sayre, and D. R. Aberle, â Lung micronodules: Automated method for detection at thinâ section CTâ Initial experience,â Radiology RADLAX --> 0033‐8419 --> 226, 256 â 262 ( 2003 ).
dc.identifier.citedreferenceK. G. Kim, J. M. Goo, J. H. Kim, H. J. Lee, B. G. Min, K. T. Bae, and J.â G. Im, â Computerâ aided diagnosis of localized groundâ glass opacity in the lung at CT: Initial experience,â Radiology RADLAX --> 0033‐8419 --> 237, 657 â 661 ( 2005 ).
dc.identifier.citedreferenceS. Armato, F. Li, M. Giger, H. MacMahon, S. Sone, and K. Doi, â Lungcancer: Performance of automated lung nodule detection applied to cancers missed in a CT screening program,â Radiology RADLAX --> 0033‐8419 --> 225, 685 â 692 ( 2002 ).
dc.identifier.citedreferenceK. T. Bae, J.â S. Kim, Y.â H. Na, K. G. Kim, and J.â H. Kim, â Pulmonary nodules: Automated detection on CT images with morphologic matching algorithmâ preliminary results,â Radiology RADLAX --> 0033‐8419 --> 236, 286 â 293 ( 2005 ).
dc.identifier.citedreferenceY. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, â Automated detection of pulmonary nodules in helical CT images based on an improved templateâ matching technique,â IEEE Trans. Med. Imaging ITMID4 --> 0278‐0062 --> 10.1109/42.932744 20, 595 â 604 ( 2001 ).
dc.identifier.citedreferenceK. Kanazawa, Y. Kawata, N. Niki, H. Satoh, H. Ohmatsu, R. Kakinuma, M. Kaneko, N. Moriyama, and K. Eguchi, â Computerâ aided diagnosis for pulmonary nodules based on helical CT images,â Comput. Med. Imaging Graph. CMIGEY --> 0895‐6111 --> 22, 157 â 167 ( 1998 ).
dc.identifier.citedreferenceK. Suzuki, S. Armato, F. Li, S. Sone, and K. Doi, â Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in lowâ dose computed tomography,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.1580485 30, 1602 â 1617 ( 2003 ).
dc.identifier.citedreferenceM. F. McNittâ Gray, E. M. Hart, N. Wyckoff, J. W. Sayre, J. G. Goldin, and D. R. Aberle, â A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: Preliminary results,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.598603 26, 880 â 888 ( 1999 ).
dc.identifier.citedreferenceS. K. Shah, M. F. McNittâ Gray, S. R. Rogers, J. G. Goldin, R. D. Suh, J. W. Sayre, I. Petkovska, H. J. Kim, and D. R. Aberle, â Computerâ aided diagnosis of the solitary pulmonary nodule,â Acad. Radiol. 1076‐6332 --> 12, 570 â 575 ( 2005 ).
dc.identifier.citedreferenceS. G. Armato, M. B. Altman, and J. Wilkie, â Automated lung nodule classification following automated nodule detection on CT: A serial approach,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.1573210 30, 1188 â 1197 ( 2003 ).
dc.identifier.citedreferenceY. Kawata, N. Niki, H. Ohmatsu, R. Kakinuma, K. Eguchi, M. Kaneko, and N. Moriyama, â Quantitative surface characterization of pulmonary nodules based on thinâ section CT images,â IEEE Trans. Nucl. Sci. IETNAE --> 0018‐9499 --> 10.1109/23.708321 45, 2132 â 2138 ( 1998 ).
dc.identifier.citedreferenceF. Li, M. Aoyama, J. Shiraishi, H. Abe, Q. Li, K. Suzuki, R. Engelmann, S. Sone, H. MacMahon, and A. K. Doi, â Radiologistsâ performance for differentiating benign from malignant lung nodules on highâ resolution CT using computerâ estimated likelihood of malignancy,â AJR, Am. J. Roentgenol. AAJRDX --> 0361‐803X --> 183, 1209 â 1215 ( 2004 ).
dc.identifier.citedreferenceM. Aoyama, Q. Li, S. Katsuragawa, F. Li, S. Sone, and K. Doi, â Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on lowâ dose CT images,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.1543575 30, 387 â 394 ( 2003 ).
dc.identifier.citedreferenceS. G. Armato, G. McLennan, M. F. McNittâ Gray, C. R. Meyer, D. Yankelevitz, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Kazerooni, H. MacMahon et al., â Lung image database consortium: Developing a resource for the medical imaging research community,â Radiology RADLAX --> 0033‐8419 --> 232, 739 â 748 ( 2004 ).
dc.identifier.citedreferenceB. Sahiner, H. P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, â Image feature selection by a genetic algorithm: Application to classification of mass and normal breast tissue on mammograms,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.597829 23, 1671 â 1684 ( 1996 ).
dc.identifier.citedreferenceM. Kass, A. Witkin, and D. Terzopoulos, â Snakes: active contour models,â Int. J. Comput. Vis. IJCVEQ --> 0920‐5691 --> 10.1007/BF00133570 1, 321 â 331 ( 1987 ).
dc.identifier.citedreferenceD. J. Williams and M. Shah, â A fast algorithm for active contours and curvature estimation,â CVGIP: Image Understand. CIUNEJ --> 1049‐9660 --> 10.1016/1049â 9660(92)90003â L 55, 14 â 26 ( 1992 ).
dc.identifier.citedreferenceC. S. Poon and M. Braun, â Image segmentation by a deformable contour model incorporating region analysis,â Phys. Med. Biol. PHMBA7 --> 0031‐9155 --> 10.1088/0031â 9155/42/9/013 42, 1833 â 1841 ( 1997 ).
dc.identifier.citedreferenceB. Sahiner, N. Petrick, H. P. Chan, L. M. Hadjiiski, C. Paramagul, M. A. Helvie, and M. N. Gurcan, â Computerâ aided characterization of mammographic masses: Accuracy of mass segmentation and its effects on characterization,â IEEE Trans. Med. Imaging ITMID4 --> 0278‐0062 --> 10.1109/42.974922 20, 1275 â 1284 ( 2001 ).
dc.identifier.citedreferenceL. D. Cohen, â On active contour models and balloons,â CVGIP: Image Understand. CIUNEJ --> 1049‐9660 --> 10.1016/1049â 9660(91)90028â N 53, 211 â 218 ( 1991 ).
dc.identifier.citedreferenceT. W. Way, B. Sahiner, L. Hadjiiski, H.â P. Chan, N. Bogot, P. Cascade, E. Kazerooni, and J. A. Fessler, â Segmentation of pulmonary nodules with 3D active contour model for computerâ aided diagnosis,â in â Radiological Society of North America Scientific Assembly and Annual Meeting Program,â Oakbrook, IL: RSNA, 2003. Chicago, IL, November 30â December 5.
dc.identifier.citedreferenceW. J. Kostis, A. P. Reeves, D. F. Yankelevitz, and C. I. Henschke, â Threeâ dimensional segmentation and growthâ rate estimation of small pulmonary nodules in helical CT images,â IEEE Trans. Med. Imaging ITMID4 --> 0278‐0062 --> 10.1109/TMI.2003.817785 22, 1259 â 1274 ( 2003 ).
dc.identifier.citedreferenceJ. W. Gurney, â Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis, I. Theory,â Radiology RADLAX --> 0033‐8419 --> 186, 405 â 413 ( 1993 ).
dc.identifier.citedreferenceJ. J. Erasmus, J. E. Connolly, H. P. McAdams, and V. L. Roggli, â Solitary pulmonary nodules. I. Morphological evaluation for differentiation of benign and malignant lesions,â Radiographics 0271â 5333 20, 43 â 58 ( 2000 ).
dc.identifier.citedreferenceS. Siegelman, N. Khouri, J. W. W. Scott, F. Leo, U. Hamper, E. Fishman, and E. Zerhouni, â Pulmonary hamartoma: CT findings,â Radiology RADLAX --> 0033‐8419 --> 160, 313 â 317 ( 1986 ).
dc.identifier.citedreferenceK. Ledor, B. Fish, L. Chaise, and S. Ledor, â CT diagnosis of pulmonary hamartomas,â J. Comput. Assist. Tomogr. JCATD5 --> 0363‐8715 --> 5, 343 â 344 ( 1981 ).
dc.identifier.citedreferenceB. Sahiner, H. P. Chan, N. Petrick, M. A. Helvie, and M. M. Goodsitt, â Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.598228 25, 516 â 526 ( 1998 ).
dc.identifier.citedreferenceM. M. Galloway, â Texture classification using gray level run lengths,â Comput. Graph. Image Process. CGIPBG --> 0146‐664X --> 4, 172 â 179 ( 1975 ).
dc.identifier.citedreferenceB. R. Dasarathy and E. B. Holder, â Image characterizations based on joint grayâ level runâ length distributions,â Pattern Recogn. Lett. PRLEDG --> 0167‐8655 --> 12, 497 â 502 ( 1991 ).
dc.identifier.citedreferenceH. P. Chan, D. Wei, M. A. Helvie, B. Sahiner, D. D. Adler, M. M. Goodsitt, and N. Petrick, â Computerâ aided classification of mammographic masses and normal tissue: Linear discriminant analysis in texture feature space,â Phys. Med. Biol. PHMBA7 --> 0031‐9155 --> 10.1088/0031â 9155/40/5/010 40, 857 â 876 ( 1995 ).
dc.identifier.citedreferenceW. Spendley, G. R. Hext, and F. R. Himsworth, â Sequential application of simplex designs in optimization and evolutionary operation,â Technometrics TCMTA2 --> 0040‐1706 --> 4, 441 â 461 ( 1962 ).
dc.identifier.citedreferenceC. E. Metz, â ROC methodology in radiologic imaging,â Invest. Radiol. INVRAV --> 0020‐9996 --> 21, 720 â 733 ( 1986 ).
dc.identifier.citedreferenceB. Sahiner, H. P. Chan, N. Petrick, M. A. Helvie, and M. M. Goodsitt, â Design of a highâ sensitivity classifier based on a genetic algorithm: Application to computerâ aided diagnosis,â Phys. Med. Biol. PHMBA7 --> 0031‐9155 --> 10.1088/0031â 9155/43/10/014 43, 2853 â 2871 ( 1998 ).
dc.identifier.citedreferenceM. M. Goodsitt, H. P. Chan, J. T. Lydick, C. R. Gandra, N. G. Chen, M. A. Helvie, J. Bailey, M. A. Roubidoux, C. E. Blane, B. Sahiner et al., â An observer study comparing spot imaging regions selected by radiologists and a computer for an automated stereo spot mammography technique,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.1737492 31, 1558 â 1567 ( 2004 ).
dc.identifier.citedreferenceC. E. Metz, B. A. Herman, and J. H. Shen, â Maximumâ likelihood estimation of receiver operating characteristic (ROC) curves from continuouslyâ distributed data,â Stat. Med. 0277‐6715 --> 17, 1033 â 1053 ( 1998 ).
dc.identifier.citedreferenceB. B. Tan, K. R. Flaherty, E. A. Kazerooni, and M. D. Iannettoni, â The solitary pulmonary nodule,â Chest CHETBF --> 0012‐3692 --> 123, 89 â 96 ( 2003 ).
dc.identifier.citedreferenceD. Ost and A. Fein, â Evaluation and management of the solitary pulmonary nodule,â Am. J. Respir. Crit. Care Med. 1073â 449X 162, 782 â 787 ( 2000 ).
dc.identifier.citedreferenceâ American Cancer Society, www.cancer.org 2005,â â Cancer Facts & Figures 2005.â
dc.identifier.citedreferenceS. J. Swensen, R. W. Viggiano, D. E. Midthun, N. L. Müller, A. Sherrick, K. Yamashita, D. P. Naidich, E. F. Patz, T. E. Hartman, J. R. Muhm et al., â Lung nodule enhancement at CT: Multicenter study,â Radiology RADLAX --> 0033‐8419 --> 214, 73 â 80 ( 2000 ).
dc.identifier.citedreferenceC. I. Henschke, D. I. McCauley, D. F. Yankelevitz, D. P. Naidich, G. McGuinness, O. S. Miettinen, D. M. Libby, M. W. Pasmantier, J. Koizumi, N. K. Altorki et al., â Early lung cancer action project: Overall design and findings from baseline screening,â Lancet LANCAO --> 0140‐6736 --> 10.1016/S0140â 6736(99)06093â 6 354, 99 â 105 ( 1999 ).
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
mp7129.pdf
Size:
248.2KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.