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An Efficient Method for Deformable Segmentation of 3D US Prostate Images

  • Yiqiang Zhan
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3150)

Abstract

We previously proposed a deformable model for automatic and accurate segmentation of prostate boundary from 3D ultrasound (US) images by matching both prostate shapes and tissue textures in US images[6]. Textures were characterized by a Gabor filter bank and further classified by support vector machines (SVM), in order to discriminate the prostate boundary from the US images. However, the step of tissue texture characterization and classification is very slow, which impedes the future applications of the proposed approach in clinic applications. To overcome this limitation, we firstly implement it in a 3-level multi-resolution framework, and then replace the step of SVM-based tissue classification and boundary identification by a Zernike moment-based edge detector in both low and middle resolutions, for fast capturing boundary information. In the high resolution, the step of SVM-based tissue classification and boundary identification is still kept for more accurate segmentation. However, SVM is extremely slow for tissue classification as it usually needs a large number of support vectors to construct a complicated separation hypersurface, due to the high overlay of texture features of prostate and non-prostate tissues in US images. To increase the efficiency of SVM, a new SVM training method is designed by effectively reducing the number of support vectors. Experimental results show that the proposed method is 10 times faster than the previous one, yet without losing any segmentation accuracy.

Keywords

Support Vector Zernike Moment Deformable Model TRUS Image Tissue Classification 
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.

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References

  1. 1.
    Overview: Prostate Cancer (2004), http://www.cancer.org
  2. 2.
    Ghanei, H., Soltanian-Zadeh, A., Ratkesicz, F.Y.: A three-dimensional deformable model for segmentation of human prostate from ultrasound image. M ed. Phy. 28, 2147–2153 (2001)Google Scholar
  3. 3.
    Hu, D.D., Fenster, A., Ladak, H.: Prostate surface segmentation from 3D ultrasound images. In: ISBI, Washington, D.C, pp. 613–616 (2002)Google Scholar
  4. 4.
    Shao, Ling, K.V., Ng, W.S.: 3D Prostate Surface Detection from Ultrasound Images Based on Level Set Method. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 389–396. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Gong, L., Pathak, S.D., Haynor, D.R., Cho, P.S., Kim, Y.: Param Using Deformable Superellipses for Prostate Segmentation. TMI 23, 340–349 (2004)Google Scholar
  6. 6.
    Zhan, Y., Shen, D.: Automated Segmentation of 3D US Prostate Images Using Statis-tical Texture-Based Matching Method. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 688–696. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Chalermwat, P., El-Ghazaw, T.: Multi-resolution Image Registration Using Genetics. In: ICIP, Japan (October 1999)Google Scholar
  8. 8.
    Shen, D., Davatzikos, C.: HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration. IEEE Trans. on Medical Imaging 21(11), 1421–1439 (2002)CrossRefGoogle Scholar
  9. 9.
    Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Trans. on Pattern Anal. Mach. Intell. 18, 837–842 (1996)CrossRefGoogle Scholar
  10. 10.
    Ghosal, S., Mehrotra, R.: Orthogonal Moment Operators for Subpixel Edge Detec-tion. Pattern Recognition 26, 295–305 (1993)CrossRefGoogle Scholar
  11. 11.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  12. 12.
    Osuna, E., Girosi, F.: Reducing the run-time complexity of Support Vector Machines. In: ICPR, Brisbane, Australia (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yiqiang Zhan
    • 1
    • 2
    • 3
  • Dinggang Shen
    • 1
    • 2
  1. 1.Sect. of Biomedical Image Analysis, Dept. of RadiologyUniversity of PennsylvaniaPhiladelphia
  2. 2.Center for Computer-Integrated Surgical Systems and TechnologyJohns Hopkins UniversityBaltimore
  3. 3.Dept. of Computer ScienceJohns Hopkins UniversityBaltimore

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