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International Symposium on Visual Computing

ISVC 2010: Advances in Visual Computing pp 209–218Cite as

Segmentation of Abdominal Organs Incorporating Prior Knowledge in Small Animal CT

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6455))

Abstract

For quantification of drug’s delivery using small animals measuring biochemical changes in abdominal organs based on functional images is essential. However, in those images, the object boundaries are not clearly enough to locate its shape and position. And even though the structural information is compensated using image registration technique, delineation of organs is difficult and time-consuming. So we suggested an automatic procedure for delineation of organs in mouse PET image with the aid of atlas as a priori anatomical information. Prior information was given by voxel label number. CT used to construct an atlas is transformed to match mouse CT to be segmented. For each label corresponding voxels represent the same organ. Then, mouse CT-PET pairs should be aligned to identify organ area in PET. After all images are aligned and fused each other both structural and functional information can be observed simultaneously for several organs.

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References

  1. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Intl. J. of Computer Vision, 321–331 (1988)

    Google Scholar 

  2. Mdeical Image Segmentation: Methods and Software, Proceedings of NFSI & ICFBI (2007)

    Google Scholar 

  3. Zhu, G., Zhang, S., Zeng, Q., Wang, C.: Directional geodesic active contour for image segmentation. J. of Electronic Imaging (2007)

    Google Scholar 

  4. Liu, C., Ma, J., Ye, G.: Medical image segmentation by geodesic active contour incorporating region statistical information. In: Intl. Conf. on Fuzzy Systems and Knowledge Discovery (2007)

    Google Scholar 

  5. Rousson, M., Deriche, R.: Dynamic segmentation of vector valued images. In: Level Methods in Imaging, Vision and Graphics. Springer, Heidelberg (2003)

    Google Scholar 

  6. Tohlfing, T., Brandt, T., Menzel, R., Maurer, C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21, 1428–1442 (2004)

    Article  Google Scholar 

  7. Maintz, J.B., et al.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)

    Article  Google Scholar 

  8. Maroy, R., Boisgard, R., Comtat, C., Frouin, V., Cathier, P., Duchesnay, E., Dolle, F., Nielsen, P.E., Trebossen, R., Tavitian, B.: Segmentation of rodent whole-body dynamic PET images: an unsupervised method based on voxel dynamics. IEEE Trans. on Med. Img. 27(3), 342–354 (2008)

    Article  Google Scholar 

  9. Jan, M.-L., et al.: A three-Dimensional Registration Method for Automated Fusion of micro PET-CT-SPECT Whole-Body Images. IEEE Trans. on Med. Img. 24(7), 886–893 (2005)

    Article  Google Scholar 

  10. Chow, P.L., et al.: A Method of Image Registration for Animal, Multi-modality Imaging. Physics in Medicine and Biology 51, 379–390 (2006)

    Article  Google Scholar 

  11. Rowland, D.J., et al.: Registration of 18f-FDG microPET and small-animal MRI. Nuclear Medicine and Biology 32, 567–572 (2006)

    Article  Google Scholar 

  12. Shen, D., et al.: Coregistration of Magnetic Resonance and Single Photon Emission Computed Tomography Images for Noninvasive Localization of Stem Cells Grafted in the Infarcted Rat Myocardium. Mol. Imaging Biol. 9, 24–31 (2007)

    Article  Google Scholar 

  13. Dogdas, B., Stout, D., Chatziioannou, A., Leahy, R.M.: Digimouse: A 3D Whole Body Mouse Atlas from CT and Cryosection Data. Phys. Med. Biol. 52(3), 577–587 (2007)

    Article  Google Scholar 

  14. Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context. Radiotherapy and Oncology 87, 93–99 (2007)

    Google Scholar 

  15. Viola, P., Wells, W.: Alignment by maximization of mutual information. International Journal of Computer Vision 24(2), 137–154 (1997)

    Article  Google Scholar 

  16. Studholme, V., Hill, D., Hawkes, D.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1), 71–86 (1999)

    Article  Google Scholar 

  17. Kakadiaris, I.A., Bello, M., Arunachalam, S., Kang, W., Ju, T., Warren, J., Carson, J., Chiu, W., Thaller, C., Eichele, G.: Landmark-driven, atlas-based segmentation of mouse brain tissue images containing gene expression data. In: Proc. Medical Image Computing and Computer-Assisted Intervention, Saint-Malo, France, pp. 192–199 (2004)

    Google Scholar 

  18. Suri, J., Wilson, D.L., Laximinarayan, S.: The handbook of medical image analysis: segmentation and registration models. Springer, Heidelberg (2005)

    Book  Google Scholar 

  19. Evaluation of atlas-based segmentation of hippocampal in healthy humans. Magnetic Resonance Imaging (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea

    SooMin Song & Myoung-Hee Kim

  2. Center for Computer Graphics and Virtual Reality, Ewha Womans University, Seoul, Korea

    Myoung-Hee Kim

Authors
  1. SooMin Song
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  2. Myoung-Hee Kim
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Ronald Chung

  6. Dyna Vox Systems, Pittsburgh, PA, USA

    Riad Hammound

  7. King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

  8. Hewlett Packard Labs, Paolo Alto, CA, USA

    Tan Kar-Han

  9. The Ohio State University, Columbus, OH, USA

    Roger Crawfis

  10. Virtual Reality Lab, EPFL, Lausanne, Switzerland

    Daniel Thalmann

  11. NASA Ames Research Center, Clifton Park, NY, USA

    David Kao

  12. Kitware, Clifton Park, NY, USA

    Lisa Avila

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© 2010 Springer-Verlag Berlin Heidelberg

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Cite this paper

Song, S., Kim, MH. (2010). Segmentation of Abdominal Organs Incorporating Prior Knowledge in Small Animal CT. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_22

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  • DOI: https://doi.org/10.1007/978-3-642-17277-9_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17276-2

  • Online ISBN: 978-3-642-17277-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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