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Automated Segmentation of Brain Tumors in MRI Using Force Data Clustering Algorithm

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

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Abstract

In this paper, we present a novel automated method for detecting tumor location in brain magnetic resonance (MR) images, and identifying the tumor boundary. This method employs an unsupervised learning algorithm called Force for coarse detection of the tumor region. Once tumor area is identified, further processing is done in the local neighborhood of the tumor to determine its boundary. The Force method, which is based on the rules of electrostatics, is used for finding spatial clusters of high intensity in the 2D space of MR image. Further analysis of the identified clusters is performed to select the cluster that contains the tumor. This method outperforms many existing methods due to its accuracy and speed. The performance of the proposed method has been verified by examining MR images of different patients.

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Author information

Authors and Affiliations

  1. School of Engineering Science, Simon Fraser University, Canada

    Masoumeh Kalantari Khandani

  2. Electrical Engineering and Computer Sciences, University of California, Berkeley, USA

    Ruzena Bajcsy & Yaser P. Fallah

  3. Institute of Transportation Studies, University of California, Berkeley, USA

    Yaser P. Fallah

Authors
  1. Masoumeh Kalantari Khandani
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  2. Ruzena Bajcsy
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  3. Yaser P. Fallah
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Editor information

Editors and Affiliations

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

    George Bebis

  2. NASA Ames Research Center, 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. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, Saitama, Japan

    Yoshinori Kuno

  6. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, P.O. Box, 119613, Singapore

    Junxian Wang

  7. Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.

    Jun-Xuan Wang

  8. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  9. Department of Informatics, Univ. of Zurich, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  10. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  11. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  12. Humana Inc., 40202, Louisville, KY, USA

    Miguel L. Encarnação

  13. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  14. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Khandani, M.K., Bajcsy, R., Fallah, Y.P. (2009). Automated Segmentation of Brain Tumors in MRI Using Force Data Clustering Algorithm. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_30

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  • DOI: https://doi.org/10.1007/978-3-642-10331-5_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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