Automated classification of lung bronchovascular anatomy in CT using AdaBoost

Medical Image Analysis

Volumn 11, Issue 3, 2007, Pages 315-324

  Ochs, Robert A ^a   Goldin, Jonathan G ^a   Abtin, Fereidoun ^a   Kim, Hyun J ^a   Brown, Kathleen ^a   Batra, Poonam ^a   Roback, Donald ^a   McNitt Gray, Michael F ^a   Brown, Matthew S ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

AdaBoost; Bronchovascular segmentation; Computed tomography; Computer aided diagnosis

Indexed keywords

COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; EIGENVALUES AND EIGENFUNCTIONS; LEARNING SYSTEMS; MATRIX ALGEBRA; PULMONARY DISEASES;

AUTOMATED CLASSIFICATION; BRONCHOVASCULAR SEGMENTATION; LUNG IMAGE DATABASE CONSORTIUM;

MEDICAL IMAGING;

CONTRAST MEDIUM;

ARTICLE; AUTOMATION; CLASSIFICATION ALGORITHM; CLASSIFIER; CLINICAL ARTICLE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; CONTRAST ENHANCEMENT; CONTROLLED STUDY; DIAGNOSTIC VALUE; HUMAN; IMAGE RECONSTRUCTION; INFORMATION PROCESSING; LUNG BLOOD VESSEL; LUNG DISEASE; LUNG LOBE; LUNG NODULE; LUNG PARENCHYMA; MACHINE LEARNING; PRIORITY JOURNAL; TRACHEA;

ALGORITHMS; CONTRAST MEDIA; HUMANS; LUNG; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; ROC CURVE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 34248679591     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2007.03.004     Document Type: Article

References (30)

1. Agam G., Armato III S.G., and Wu C. Vessel tree reconstruction in thoracic CT scans with application to nodule detection. IEEE Trans. Med. Imag. 24 4 (2005) 486-499
2. Armato III S.G., Giger M., and MacMahon H. Automated detection of lung nodules in CT scans: preliminary results. Med. Phys. 28 (2001) 1552-1561
3. Brown M.S., McNitt-Gray M.F., Mankovich N.J., Goldin J.G., Hiller J., Wilson L.S., and Aberle D.R. Method for segmenting chest CT image data using an anatomical model: Preliminary results. IEEE Trans. Med. Imag. 16 6 (1997) 828-839
4. Brown M.S., Goldin J.G., Suh R.D., McNitt-Gray M.F., Sayre J.W., and Aberle D.R. Lung micronodules: automated method for detection at thin-section CT-initial experience. Radiology 226 (2003) 256-262
5. Brushberg J., Seibert J., Leidholdt Jr. E., and Boone J. The Essential Physics of Medical Imaging (2002), Lippincott Williams, and Wilkins, Philadelpha
6. Cootes T., Taylor C., Cooper D., and Graham J. Active shape models - their training and application. Comput Vis Image Understanding. 61 1 (1995) 38-59
7. Fiebich M., Wiethold C., Renger B., Armato III S., Hoffmann K., Wormanns D., and Diederich S. Automatic detection of pulmonary nodules in low-dose screening thoracic CT examinations. Proc. SPIE 3661 (1999) 1434-1439
8. Florack L., ter Haar Romeny B., Koenderink J., and Viergever M. Scale and the differential structure of images. Imag. Vis. Comp. 10 6 (1992) 376-388
9. Food and Drug Administration, 2006. Critical Path Opportunities List, available from: http://www.fda.gov/oc/initiatives/criticalpath/reports/opp_list.pdf.
10. Frangi A., Niessen W., Vincken K., and Viergever M. Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science 1496 (1998) 130-137
11. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning, and an application to boosting. J. Comput. Syst. Sci. 55 1 (1997) 119-139
12. Krissian K., Malandain G., Ayache N., Vaillant R., and Trousset Y. Model Based Detection of Tubular Structures in 3D Images. Comput. Vision Image Understanding 80 (2000) 130-171
13. Lindeberg T. Scale-Space Theroy in Computer Vision (1994), Kluwer Academic Publishers., Dordrecht, Netherlands
14. Lung Image Database Consortium, 2006. LIDC Release 0001. ftp://ncicbfalcon.nci.nih.gov/lidc/LIDC_Release_0001 (since moved to https://imaging.nci.nih.gov/ncia/).
15. Malladi R., Sethian J., and Vemuri B. Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17 2 (1995) 158-175
16. McInerney T., and Terzopolous D. Deformable models in medical image analysis: a survey. Med. Image Anal. 1 2 (1996) 91-108
17. Ochs R., Kim H., Angel E., Panknin C., McNitt-Gray M., and Brown M. Impact of LIDC reader agreement on the reference dataset and reported CAD sensitivity. Proc. SPIE (2007) 6514-6582
18. Palágyi K., Tschirren J., Hoffman E.A., and Sonka M. Quantitative analysis of pulmonary airway tree structures. Comput. Biol. Med. July 30 (2005)
19. Park W., Hoffman EA., and Sonka M. Segmentation of intrathoracic airway trees: A fuzzy logic approach. IEEE Trans. Med. Imag. 17 4 (1998) 489-497
20. Pizer S., Gerig G., Joshi S., and Aylward S. Multiscale medial shape-based analysis of image objects. Proc. IEEE 91 10 (2003) 1670-1679
21. Pock, T., Janko, C., Beichel, R., Bischof, H., 2005. Multiscale medialness for robust segmentation of 3D tubular structures, in: Proc. of the Computer Vision Winter Workshop 2005.
22. Saita S., Yasutomo M., Kubo M., Kawata Y., Niki N., Eguchi K., Ohmatsu H., Kakinuma R., Kaneko M., Kusumoto M., Moriyama N., and Sasagawa M. An extraction algorithm of pulmonary fissures from mutli-slice CT image. Proc. SPIE 5370 (2004) 1590-1597
23. Sato Y., Nakajima S., Shiraga N., Atsumi H., Yoshida S., Koller T., Gerig G., and Kikinis R. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med. Image Anal. 2 2 (1998) 143-168
24. Schapire, R., 2001. The boosting approach to machine learning: an overview, in: Proc. of MSRI Workshop on Nonlinear Estimation and Classification, Berkeley, CA.
25. van Rikxoort E., and van Ginneken B. A pattern recognition approach to enhancing structures in 3D CT data. Proc. SPIE 6144 (2006) 61441O
26. Viola P., and Jones M. Rapid object detection using a boosted cascade of simple features. IEEE CVPR 1 (2001) I-511-I-518
27. Wiemker R., Bülow T., and Blaffert T. Unsupervised extraction of the pulmonary interlobar fissures from high resolution thoracic CT data. Comput. Assist. Radiol. Surgery Berlin 2005 (2005) 1121-1126
28. Zhang L., Hoffman E., and Reinhardt J. Atlas-driven lung lobe segmentation in volumetric X-ray CT images. IEEE Trans. Med. Imag. 25 1 (2006) 1-16
29. Zhou X., Hayashi T., Hara T., Fujita H., Yokoyama R., Kiryu T., and Hosi H. Automatic recognition of lung lobes and fissures from multi-slice CT images. Proc. SPIE 5370 (2004) 1629-1633
30. Zhu S., and Yuille A. FORMS: A flexible object recognition and modelling system. Int. J. Comput. Vis. 20 3 (1996) 187-212