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Recognition of Protruding Objects in Highly Structured Surroundings by Structural Inference

  • Vincent F. van Ravesteijn
  • Frans M. Vos
  • Lucas J. van Vliet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

Recognition of objects in highly structured surroundings is a challenging task, because the appearance of target objects changes due to fluctuations in their surroundings. This makes the problem highly context dependent. Due to the lack of knowledge about the target class, we also encounter a difficulty delimiting the non-target class. Hence, objects can neither be recognized by their similarity to prototypes of the target class, nor by their similarity to the non-target class. We solve this problem by introducing a transformation that will eliminate the objects from the structured surroundings. Now, the dissimilarity between an object and its surrounding (non-target class) is inferred from the difference between the local image before and after transformation. This forms the basis of the detection and classification of polyps in computed tomography colonography. 95% of the polyps are detected at the expense of four false positives per scan.

Keywords

Feature Space Compute Tomography Colonography Dissimilarity Measure Candidate Object Virtual Colonoscopy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Duin, R.P.W., Pekalska, E.: Structural inference of sensor-based measurements. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 41–55. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition, Foundations and Applications. World Scientific, Singapore (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    van Wijk, C., van Ravesteijn, V.F., Vos, F.M., Truyen, R., de Vries, A.H., Stoker, J., van Vliet, L.J.: Detection of protrusions in curved folded surfaces applied to automated polyp detection in CT colonography. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 471–478. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Ferrucci, J.T.: Colon cancer screening with virtual colonoscopy: Promise, polyps, politics. American Journal of Roentgenology 177, 975–988 (2001)CrossRefGoogle Scholar
  5. 5.
    Winawer, S., Fletcher, R., Rex, D., Bond, J., Burt, R., Ferrucci, J., Ganiats, T., Levin, T., Woolf, S., Johnson, D., Kirk, L., Litin, S., Simmang, C.: Colorectal cancer screening and surveillance: Clinical guidelines and rationale – update based on new evidence. Gastroenterology 124, 544–560 (2003)CrossRefGoogle Scholar
  6. 6.
    van Wijk, C., van Ravesteijn, V.F., Vos, F.M., van Vliet, L.J.: Detection and segmentation of protruding regions on folded iso-surfaces for the detection of colonic polyps (submitted)Google Scholar
  7. 7.
    Konukoglu, E., Acar, B., Paik, D.S., Beaulieu, C.F., Rosenberg, J., Napel, S.: Polyp enhancing level set evolution of colon wall: Method and pilot study. IEEE Trans. Med. Imag. 26(12), 1649–1656 (2007)CrossRefGoogle Scholar
  8. 8.
    Pekalska, E., Duin, R.P.W.: Learning with general proximity measures. In: Proc. PRIS 2006, pp. IS15–IS24 (2006)Google Scholar
  9. 9.
    Summers, R.M., Yao, J., Pickhardt, P.J., Franaszek, M., Bitter, I., Brickman, D., Krishna, V., Choi, J.R.: Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population. Gastroenterology 129, 1832–1844 (2005)CrossRefGoogle Scholar
  10. 10.
    Summers, R.M., Handwerker, L.R., Pickhardt, P.J., van Uitert, R.L., Deshpande, K.K., Yeshwant, S., Yao, J., Franaszek, M.: Performance of a previously validated CT colonography computer-aided detection system in a new patient population. AJR 191, 169–174 (2008)CrossRefGoogle Scholar
  11. 11.
    Näppi, J., Yoshida, H.: Fully automated three-dimensional detection of polyps in fecal-tagging CT colonography. Acad. Radiol. 14, 287–300 (2007)CrossRefGoogle Scholar
  12. 12.
    van Ravesteijn, V.F., van Wijk, C., Truyen, R., Peters, J.F., Vos, F.M., van Vliet, L.J.: Computer aided detection of polyps in CT colonography: An application of logistic regression in medical imaging (submitted) Google Scholar
  13. 13.
    Serlie, I.W.O., Vos, F.M., Truyen, R., Post, F.H., van Vliet, L.J.: Classifying CT image data into material fractions by a scale and rotation invariant edge model. IEEE Trans. Image Process. 16(12), 2891–2904 (2007)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Serlie, I.W.O., de Vries, A.H., Vos, F.M., Nio, Y., Truyen, R., Stoker, J., van Vliet, L.J.: Lesion conspicuity and efficiency of CT colonography with electronic cleansing based on a three-material transition model. AJR 191(5), 1493–1502 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Vincent F. van Ravesteijn
    • 1
  • Frans M. Vos
    • 1
    • 2
  • Lucas J. van Vliet
    • 1
  1. 1.Quantitative Imaging Group, Faculty of Applied SciencesDelft University of TechnologyThe Netherlands
  2. 2.Department of RadiologyAcademic Medical CenterAmsterdamThe Netherlands

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