Multi-Attribute Non-initializing Texture Reconstruction Based Active Shape Model (MANTRA)

  • Robert Toth
  • Jonathan Chappelow
  • Mark Rosen
  • Sona Pungavkar
  • Arjun Kalyanpur
  • Anant Madabhushi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)

Abstract

In this paper we present MANTRA (Multi-Attribute, Non-Initializing, Texture Reconstruction Based Active Shape Model) which incorporates a number of features that improve on the the popular Active Shape Model (ASM) algorithm. MANTRA has the following advantages over the traditional ASM model. (1) It does not rely on image intensity information alone, as it incorporates multiple statistical texture features for boundary detection. (2) Unlike traditional ASMs, MANTRA finds the border by maximizing a higher dimensional version of mutual information (MI) called combined MI (CMI), which is estimated from kNN entropic graphs. The use of CMI helps to overcome limitations of the Mahalanobis distance, and allows multiple texture features to be intelligently combined. (3) MANTRA does not rely on the mean pixel intensity values to find the border; instead, it reconstructs potential image patches, and the image patch with the best reconstruction based on CMI is considered the object border. Our algorithm was quantitatively evaluated against expert ground truth on almost 230 clinical images (128 1.5 Tesla (T) T2 weighted in vivo prostate magnetic resonance (MR) images, 78 dynamic contrast enhanced breast MR images, and 21 3T in vivo T1-weighted prostate MR images) via 6 different quantitative metrics. Results from the more difficult prostate segmentation task (in which a second expert only had a 0.850 mean overlap with the first expert) show that the traditional ASM method had a mean overlap of 0.668, while the MANTRA model had a mean overlap of 0.840.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  2. 2.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    van Ginneken, B., Frangi, A.F., Staal, J.J., et al.: Active shape model segmentation with optimal features. IEEE Trans. Med. Imag. 21(8), 924–933 (2002)CrossRefGoogle Scholar
  4. 4.
    Toth, R., Tiwari, P., Rosen, M., Kalyanpur, A., Pungabkar, S., Madabhushi, A.: A multi-modal prostate segmentation scheme by combining spectral clustering and active shape models. In: SPIE, vol. 6914, pp. 69144S1–69144S12 (2008)Google Scholar
  5. 5.
    Seghers, D., Loeckx, D., Maes, F., Vandermeulen, D., Suetens, P.: Minimal shape and intensity cost path segmentation. IEEE Trans. Med. Imag. 26(8), 1115–1129 (2007)CrossRefGoogle Scholar
  6. 6.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imag. 22(8), 986–1004 (2003)CrossRefGoogle Scholar
  7. 7.
    Madabhushi, A., Feldman, M., Metaxas, D., Tomaszeweski, J., Chute, D.: Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI. IEEE Trans. Med. Imag. 24(12), 1611–1625 (2005)CrossRefGoogle Scholar
  8. 8.
    Doyle, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: A boosting cascade for automated detection of prostate cancer from digitized histology. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 504–511. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Viswanath, S., Rosen, M., Madabhushi, A.: A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery. In: SPIE (2008)Google Scholar
  10. 10.
    Chappelow, J., Madabhushi, A., Rosen, M., Tomaszeweski, J., Feldman, M.: A combined feature ensemble based mutual information scheme for robust inter-modal, inter-protocol image registration. In: ISBI 2007, pp. 644–647 (April 2007)Google Scholar
  11. 11.
    Tomazevic, D., Likar, B., Pernus, F.: Multifeature mutual information. In: Fitzpatrick, J.M., Sonka, M. (eds.) Proceedings of SPIE: Medical Imaging, vol. 5370, pp. 143–154 (2004)Google Scholar
  12. 12.
    Matsuda, H.: Physical nature of higher-order mutual information: Intrinsic correlations and frustration. Phys. Rev. E 62(3), 3096–3102 (2000)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Kraskov, A., Stögbauer, H., Grassberger, P.: Estimating mutual information. Phys. Rev. E 69(6), 066138 (2004)CrossRefGoogle Scholar
  14. 14.
    Cootes, T., Taylor, C., Lanitis, A.: Evaluating of a multi-resolution method for improving image search. In: Proc. British Machine Vision Conference, pp. 327–336 (1994)Google Scholar
  15. 15.
    Madabhushi, A., Metaxas, D.N.: Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans. Med. Imag. 22(2), 155–170 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Robert Toth
    • 1
  • Jonathan Chappelow
    • 1
  • Mark Rosen
    • 2
  • Sona Pungavkar
    • 3
  • Arjun Kalyanpur
    • 4
  • Anant Madabhushi
    • 1
  1. 1.RutgersThe State University of New JerseyNew BrunswickUSA
  2. 2.University of PennsylvaniaPhiladelphiaUSA
  3. 3.Dr. Balabhai Nanavati HospitalMumbaiIndia
  4. 4.Teleradiology SolutionsBangaloreIndia

Personalised recommendations