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Biomedical Engineering / Biomedizinische Technik

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering and the German Society of Biomaterials

Editor-in-Chief: Dössel, Olaf

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Volume 59, Issue 3


Volume 57 (2012)

Level set method coupled with Energy Image features for brain MR image segmentation

Mirela (Visan) Punga
  • Aurel Vlaicu High School, 1 Decembrie 1918 St., 800511 Galati, Romania
  • Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunarea de Jos University of Galati, 47 Domneasca St., 800008 Galati, Romania
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Rahul Gaurav
  • Advanced Research and Techniques for Multidimensional Imaging Systems, Télécom Sud Paris, 9 rue Charles Fourier, 91011 Evry Cedex, Paris, France
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Luminita Moraru
  • Corresponding author
  • Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunarea de Jos University of Galati, 47 Domneasca St., 800008 Galati, Romania
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2014-03-04 | DOI: https://doi.org/10.1515/bmt-2013-0111


Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels’ values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.

Keywords: active contour model; intensity inhomogeneity; lesion segmentation; MRI


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About the article

Corresponding author: Luminita Moraru, Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunarea de Jos University of Galati, 47 Domneasca St., 800008 Galati, Romania, Phone: +40745649014, Fax: +40236461353, E-mail:

Received: 2013-10-17

Accepted: 2014-02-10

Published Online: 2014-03-04

Published in Print: 2014-06-01

Citation Information: Biomedical Engineering / Biomedizinische Technik, Volume 59, Issue 3, Pages 219–229, ISSN (Online) 1862-278X, ISSN (Print) 0013-5585, DOI: https://doi.org/10.1515/bmt-2013-0111.

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