Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Gareth Beddoe; Richard Boyes; Xujiong Ye; Xiaoyun Yang; Greg Slabaugh (2010)
Publisher: MDPI AG
Journal: Algorithms
Languages: English
Types: Article
Subjects: CAD, Electronic computers. Computer science, T55.4-60.8, QA75, G400 Computer Science, colorectal lesion detection, QA75.5-76.95, pattern recognition, Industrial engineering. Management engineering
We present a complete, end-to-end computer-aided detection (CAD) system for identifying lesions in the colon, imaged with computed tomography (CT). This system includes facilities for colon segmentation, candidate generation, feature analysis, and classification. The algorithms have been designed to offer robust performance to variation in image data and patient preparation. By utilizing efficient 2D and 3D processing, software optimizations, multi-threading, feature selection, and an optimized cascade classifier, the CAD system quickly determines a set of detection marks. The colon CAD system has been validated on the largest set of data to date, and demonstrates excellent performance, in terms of its high sensitivity, low false positive rate, and computational efficiency.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Cancer Facts and Figures; American Cancer Society 2007 Annual Report; Atlanta, GA, USA, 2007; Volume 12, pp. 11-12.
    • 2. Zalis, M.E.; Barish, M.A.; Choi, J.R.; Dachmann, A.H.; Fenlon, H.M.; Ferrucci, J.T.; Glick, S.N.; Laghi, A.; Macari, M.; McFarland, E.G.; Morrin, M.M.; Pickhardt, P.J.; Soto, J.; Yee, J. CT Colonography Report and Data System: A consensus Proposal. Radiology 2005, 236, 3-9.
    • 3. Winawer, S. The Achievements, Impact, and Future of the National Polyp Study. Gastrointestinal Endoscopy 2006, 64, 975-978.
    • 4. Johnson, C.D.; Dachman, A.H. CT Colonography: The Next Colon Screening Examination? Radiology 2000, 216, 311-319.
    • 5. Kim, D.; Pickhardt, P.; Taylor, A.; Leung, W.; Winter, T.; Hinshaw, J.L.; Gopal, D.V.; Reichelderfer, M.; Hsu, R.H.; Pfau, P.R. CT Colonography versus Colonoscopy for the Detection of Advanced Neoplasia. NEJM 2007, 357, 1403-1412.
    • 6. Ranallo, F.N. CT Colonography: An Honest Assessment of Radiation Risks. In Proceedings of the 10th International Symposium on Virtual Colonoscopy, Hyatt Regency Reston, Reston, VA, USA, October 26-28, 2009.
    • 7. Johnson, C.D.; Chen, M.; Toledano, A.; Heiken, J.; Dachman, A.H.; Kuo, M.D.; Menias, C.; Stewert, B.; Cheema, J.I.; Obregon, R.G.; Fidler, J.L.; Zimmerman, P.; Horton, K.M.; Coakley, K.; Iyer, R.B.; Hara, A.K.; Halvorsen, R.A.; Casola, G.; Yee, J.; Herman, B.A.; Burgart, L.J.; Limburg, P.J. Accuracy of CT Colonography for Detection of Large Adenomas and Cancers. NEJM 2008, 259, 1207-1217.
    • 8. Burling, D.; Moore, A.; Marshall, M.; Weldon, J.; Gillen, C.; Baldwin, R.; Smith, K.; Pickhardt, P.; Honeyfield, L.; Taylor, S. Virtual Colonoscopy: Effect of Computer-Assisted Detection (CAD) on Radiographer Performance. Clin. Radiol. 2008, 63, 549-556.
    • 9. Lawrence, E.; Pickhardt, P.J.; Kim, D.H.; Robbins, J. Computer-Aided Detection of Colorectal Polyps: Diagnostic Performance in a Large Asymptomatic Screening Population. In Proceedings of the 10th International Symposium on Virtual Colonoscopy, Hyatt Regency Reston, Reston, VA, USA, October 26-28, 2009.
    • 10. Yoshida, H.; Nappi, J.; MacEneaney, P.; Rubin, D.T.; Dachman, A.H. Computer-aided Diagnosis Scheme for Detection of Polypsat CT Colonography. Radiographics 2002, 22, 963-979.
    • 11. Paik, D.; Beaulieu, C.; Rubin, G.; Acar, B.; Jeffrey, R.B., Jr.; Yee, J.; Dey, J.; Napel, S. Surface Normal Overlap: A Computer-Aided Detection Algorithm with Application to Colonic Polyps and Lung Nodules in Helical CT. IEEE Trans. Med. Imaging 2004, 23, 661-675.
    • 12. Sundaram, P.; Zomorodian, A.; Beaulieu, C.; Napel, S. Colon Polyp Detection using Smoothed Shape Operators: Preliminary Results. Med. Image Anal. 2008, 12, 99-119.
    • 13. Summers, R.; Yao, J.; Pickhardt, P.; Franaszek, M.; Bitter, I.; Brickman, D.; Krishna, V.; Choi, J.R. Computed Tomographic Virtual Colonoscopy Computer-Aided Polyp Detection in a Screening Population. Gastroenterology 2005, 129, 1832-1844.
    • 14. Bogoni, L.; Cathier, P.; Dundar, M.; Jerebko, A.; Lakare, S.; Liang, J.; Periaswamy, S.; Baker, M.; Macari, M. Computer-Aided Detection (CAD) for CT Colonography: A Tool to Address a Growing Need. British J. Radiol. 2005, 78, S57-S62.
    • 15. Jerebko, A.; Lakare, S.; Cathier, P.; Periaswamy, S.; Bogoni, L. Symmetric Curvature Patterns for Colonic Polyp Detection. In Proceedings of the 9th MICCAI Conferences, Copenhagen, Denmark, October 1-6, 2006.
    • 16. Bhotika, R.; Mendonca, P.; Sirohey, S.; Turner, W.; Lee, Y.; McCoy, J.; Brown, R.; Miller, J. Part-based Local Shape Models for Colon Polyp Detection. In Proceedings of the 9th MICCAI Conferences, Copenhagen, Denmark, October 1-6, 2006.
    • 17. Tu, Z.; Zhou, X.S.; Barbu, A.; Bogoni, L.; Comaniciu, D. Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA, June 17-22, 2006.
    • 18. Kim, S.H.; Lee, J.M.; Lee, J.G.; Kim, J.H.; Lefere, P.A.; Han, J.K.; Choi, B.I. Computer-Aided Detection of Colonic Polyps at CT Colonography Using a Hessian Matrix-Based Algorithm: Preliminary Study. Gastrointestinal Imaging 2007, 189, 41-51.
    • 19. Dundar, M.; Bi, J. Joint Optimization of Cascaded Classifiers for Computer Aided Detection. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, June 18-23, 2007.
    • 20. Qiu, F.; Marino, J.; Kaufman, A. Computer Aided Polyp Detection with Texture Analysis. In Proceedings of MICCAI Workshop on Computational and Visualization Challenges in the New Era of Virtual Colonoscopy, Kimmel Center, New York, NY, USA, September 6, 2008.
    • 21. Suzuki, K.; Yoshida, H.; Nappi, J.; Armato, S.G.; Dachman, A.H. Mixture of Expert 3D Massive-Training ANNs for Reduction of Multiple Types of False Positives in CAD for Detection of Polyps in CT Colonography. Med. Phys. 2008, 35, 694-703.
    • 22. Li, J.; Huang, A.; Yao, J.; Liu, J.; Uitert, R.L.V.; Petrick, N.; Summers, R.M. Optimizaing Computer-Aided Colonic Polyp Detection for CT Colonography by Evolving the Pareto Front. Med. Phys. 2009, 36, 201-212.
    • 23. Liu, J.; Wang, S.; Kabadi, S.; Summers, R.M. High Performance Computer Aided Detection System for Polyp Detection in CT Colonography with Fluid and Fecal Tagging. In Proc. SPIE, 2009, 7260, 72601B:1-72601B:7.
    • 24. van Ravesteijn, V.F.; van Wijk, C.; Vos, F.M.; Truyen, R.; Peters, J.; Stoker, J.; van Vliet, L. Computer Aided Detection of Polyps in CT Colonography using Logistic Regression. IEEE Trans. Med. Imaging 2010, in press.
    • 25. Vining, D.J.; Ge, Y.; Ahn, D.K.; Stelts, D.R. Virtual Colonscopy with Computer-Assisted Polyp Detection. In Computer-Aided Diagnosis in Medical Imaging ; Doi, K., MacMahon, H., Giger, M., Hoffman, K.R., Eds.; Elsevier Science: Maryland Heights, MO, USA, 1999; pp. 445-452.
    • 26. Yoshida, H.; Nappi, J. Three-Dimensional Computer-Aided Diagnosis Scheme for Detection of Colonic Polyps. IEEE Trans. Med. Imaging 2001, 20, 1261-1274.
    • 27. Koenderink, J.; Doorn, A.V. Surface Shape and Curvature Scales. Image Vision Comput. 1992, 10, 557-565.
    • 28. Chowdhury, T.A.; Whelan, P.F.; Ghita, O. A Fully Automatic CAD-CTC System Based on Curvature Analysis for Standard and Low Dose CT Data. IEEE Trans. Biomed. Eng. 2007, 55, 888-901.
    • 29. Lindstrom, P. Out-of-Core Simplification of Large Polygonal Models. In Proceedings of SIGGRAPH, New Orleans, LA, USA, July 23-28, 2000.
    • 30. Huang, L.K.; Wang, M.J.J. Image Thresholding by Minimizing the Measures of Fuzziness. Pattern Recogn. 1995, 28, 41-51.
    • 31. Jain, A.K. Fundamentals of Digial Image Processing; Prentice Hall: Englewood Cliffs, NJ, USA, 1989.
    • 32. Lesion Boundary Detection. UK Patent 2,415,563; Medicsight: London, UK, 2009
    • 33. Thirion, J.; Gourdon, A. Computing the Differential Characteristics of Isointensity Surface. Comput. Vis. Image Underst. 1992, 61, 190-202.
    • 34. Monga, O.; Benayoun, S. Using Partial Derivatives of 3D Images to Extract Typical Surface Features. In Proceedings of the 3rd Annual Conference of Integrating Perception, Planning and Action, Perth, Australia, July 8-10, 1992; pp. 225-236.
    • 35. Kobatake, H.; Murakami, M. Adaptive Filter to Detect Rounded Convex Regions: Iris Filter. In Proceedings of ICPR, Vienna, Austria, August 25-29, 1996.
    • 36. Wang, Z.; Li, L.; Anderson, J.; Harrington, D.; Liang, Z. Colonic Polyp Characterization and Detection Based on Both Morphological and Texture Features. In Proceedings of the 18th International Congress and Exhibition on Computer Assisted Radiology and Surgery, Chicago, IL, USA, June 23-26, 2004.
    • 37. Wang, T.; Lu, H.; Zhang, J.; Zhang, G.; Liu, X.; Computer-Aided Detection for Virtual Colonoscopy Based on 3D Texture Analysis. In Proceedings of the 22nd International Congress and Exhibition on Computer Assisted Radiology and Surgery, Barcelona, Spain, June 25-28, 2008.
    • 38. Lu, H.; Zhang, G.; Wang, T.; Jiao, C.; Wang, J.; Liang, Z. Computer-Aided Polyp Detection Based on 3D Texture Analysis for Virtual Colonoscopy. In Proceedings of the MICCAI 2008 Workshop on Computational and Visualization Challenges in the New Era of Virtual Colonoscopy, Kimmel Center, New York, NY, USA, September 6, 2008.
    • 39. Laws, K. Textured Image Segmentation. PhD thesis, University of Southern California: Los Angeles, CA, USA, 1980.
    • 40. Lohmann, G. Volumetric Image Analysis; Wiley: Hoboken, NJ, USA, 1998.
    • 41. Li, Q.; Sone, S.; Doi, K. Selective Enhancement Filters for Nodules, Vessels, and Airway Walls in Two- and Three-Dimensional CT Scans. Med. Phys. 2003, 30, 2040-2051.
    • 42. Zheng, Y.; Yang, X.; Beddoe, G. Reduction of False Positives in Polyp Detection using Weighted Support Vector Machines. In Proceedings of EMBC, Lyon, France, August 23-26, 2007.
    • 43. Viola, P.; Jones, M.J. Robust Real-Time Face Detection. Int. J. Comput. Vis. 2004, 57, 137-154.
    • 44. Sun, J.; Rehg, J.M.; Bobick, A.F. Automatic Cascade Training with Perturbation Bias. In Proceedings of CVPR, Washington, DC, USA, June 27-July 2, 2004; pp. 276-283.
    • 45. Freund, Y.; Scharpire, R.E. A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting. J. Comput. Syst. Sci. 1997, 55, 119-139.
    • 46. Peng, H.; Long, F.; Ding, C. Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27, 1226-1238.
    • 47. Battiti, R. Using Mutual Information for Selecting Features in Supervised Neural Net Learning. IEEE Trans. Neural Networks 1994, 5, 537-550.
    • 48. Kwak, N.; Choi, C.H. Input Feature Selection for Classification Problems. IEEE Trans. Neural Netw. 2002, 13, 143-157.
    • 49. He, L.; Chao, Y.; Suzuki, K.; Wu, K. Fast Connected-Component Labeling. Pattern Recogn. 2009, 42, 1977-1987.
    • 50. Barnes, E. VC CAD Nabs Undetected Polyps in Jumbo Screening Study; AuntMinnie.com: Tucson, AZ, USA, November 9, 2009.
  • No related research data.
  • No similar publications.