An Automated Segmentation Method of Kidney Using Statistical Information

  • Baigalmaa Tsagaan
  • Akinobu Shimizu
  • Hidefumi Kobatake
  • Kunihisa Miyakawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2488)

Abstract

This paper presents a deformable model based approach for automated segmentation of kidneys from tree dimensional (3D) abdominal CT images. Since the quality of an input image is very poor and noisy due to the large slice thickness, we use a deformable model represented by NURBS surface, which uses not only the gray level appearance of the target but also statistical information of the shape. A shape feature vector is defined to evaluate geometric character of the surface and its statistical information is incorporated into the deformable model through an energy formulation for deformation. Principal curvature on the model surface, which is invariant to rotation and translation, is adopted as a component of the vector. Furthermore, automated positioning procedure of an initial model is presented in this paper. We applied the proposed method to the 33 abdominal CT images whose slice thickness is 10mm and evaluated the effectiveness of the proposing method.

Reference

  1. 1.
    Duncan, J., Ayache, N.: Medical Image Analysis: Progress Over Two Decades and Challenges Ahead, IEEE Trans. Patt. Anal. Mach. Intell., vol. 22 (2000) 85–106CrossRefGoogle Scholar
  2. 2.
    McInerney, T., Terzopoulos, D.: Deformable Models in Medical Image Analysis: A Survey, Med. Imag. Anal. vol. 1(2) (1996) 91–108CrossRefGoogle Scholar
  3. 3.
    Shimizu, A.: Segmentation of Medical Images Using Deformable Models: A Survey, Med. Imag. Tech. in Japan. vol. 20(1) (2002) 3–12MathSciNetGoogle Scholar
  4. 4.
    Cootes, F., Taylor, L., Cooper, H.: Active Shape Models;Their Training and Application, CVIU vol. 61(1) (1995) 38–59Google Scholar
  5. 5.
    Jacob, G., Noble, A., Blake, A.: Evaluating a Robust Contour Tracker on Echocardiographic Sequences, Med. Imag. Anal. vol. 3(3) (1998) 63–75Google Scholar
  6. 6.
    Fleute, M., Lavallee, M., Julliard, S.: Incorporating a Statistically Based Shape Model Into a System for Computer-Assisted Anterior Cruciate Ligament Surgery, Med. Imag. Anal. vol. 3(3) (1999) 209–222CrossRefGoogle Scholar
  7. 7.
    Shen, D., Herskovits, E.H., Davatzikos, C.: An Adaptive-Focus Statistical Shape Model for Segmentation and Shape Modeling of 3-D Brain Structure, IEEE Trans. Med. Imag., vol. 20(4) (2001) 257–270CrossRefGoogle Scholar
  8. 8.
    Hamarneh, G., McInerney, T., Terzopoulos, D.: Deformable Organisms For Automatic Medical Image Analysis, MICCAI (2001) 66–76Google Scholar
  9. 9.
    Staib, L.H., Duncan, J.S.: Boundary Finding with Parametrically Deformable Models, IEEE Trans. Patt. Anal. Mach. Intell., vol. 14(11) (1992) 1061–1075CrossRefGoogle Scholar
  10. 10.
    Szekely, G., Kelemen, A.: Segmentation of 2-D and 3-D Objects From MRI Volume Data Using Constrained Elastic Deformations of Flexible Fourier Contour and Surface Models, Med. Imag.Anal., vol. 1(1) (1996) 19–34Google Scholar
  11. 11.
    Tsagaan, B., Shimizu, A., Kobatake, H., Miyakawa, K., Hanzawa, Y.: Segmentation of Kidney by Using Deformable Model, ICIP, vol. 3 (2001) 1059–1062Google Scholar
  12. 12.
    Terzopoulos, D., Qin, H.,: Dynamic NURBS with Geometric Constraints for Interactive Sculpting”, ACM Trans. Graphics, vol. 13(2) (1994) 103–136MATHCrossRefGoogle Scholar
  13. 13.
    Tsagaan, B., Shimizu, A., Kobatake, H., Miyakawa, K.: Development of Extraction Method of Kidneys From Abdominal CT Images Using 3-D Deformable Model, Trans. of IEICE in Japan, vol. J85(D-II) (2002) 140–148Google Scholar
  14. 14.
    Williams, D., Shan, D., Shan, M.,: A Fast Algorithm for Active Contours, CVGIP:Imag. Under., vol. 55(1) (1992) 14–26MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Baigalmaa Tsagaan
    • 1
  • Akinobu Shimizu
    • 1
  • Hidefumi Kobatake
    • 1
  • Kunihisa Miyakawa
    • 2
  1. 1.Graduate School of Bio-Applications and Systems EngineeringTokyo University of Agriculture and TechnologyTokyoJapan
  2. 2.Department of RadiologyNational Cancer Center HospitalTokyoJapan

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