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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

Editorial Board: Augat, Peter / Habibović, Pamela / Haueisen, Jens / Jahnen-Dechent, Wilhelm / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Lenarz, Thomas / Leonhardt, Steffen / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Boenick, Ulrich / Jaramaz, Branislav / Kraft, Marc / Lenthe, Harry / Lo, Benny / Mainardi, Luca / Micera, Silvestro / Penzel, Thomas / Robitzki, Andrea A. / Schaeffter, Tobias / Snedeker, Jess G. / Sörnmo, Leif / Sugano, Nobuhiko / Werner, Jürgen /

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Online
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1862-278X
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Volume 59, Issue 3

Issues

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

Abstract

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

References

  • [1]

    Angelini ED, Song T, Mensh BD, Laine A. Segmentation an quantitative evaluation of the brain MRI data with a multi-phase three-dimensional implicit deformable model, In: Fitzpatrick JM, Sonka M, editors. Medical imaging: image processing. Proceedings of SPIE, vol 5370, Bellinham, WA 2004.Google Scholar

  • [2]

    Balafar MA, Ramli AR, Saripan MI, Mashohor S. Review of brain MRI image segmentation methods. Artif Intell Rev 2010; 33: 261–274.Web of ScienceCrossrefGoogle Scholar

  • [3]

    Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process 2001; 10: 266–277.PubMedCrossrefGoogle Scholar

  • [4]

    Ciecholewski M, Chocholowicz J. Gallbladder shape extraction from ultrasound images using active contour models. Comput Biol Med 2013; 43: 2238–2255.Web of SciencePubMedCrossrefGoogle Scholar

  • [5]

    Elter M, Held C, Wittenberg T. Contour tracing for segmentation of mammographic masses. Phys Med Biol 2010; 55: 5299–5315.Web of ScienceCrossrefPubMedGoogle Scholar

  • [6]

    Eskildsen SF, Coupé P, Fonov V, et al. BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage 2012; 59: 2362–2373.PubMedWeb of ScienceCrossrefGoogle Scholar

  • [7]

    Li BN, Chui CK, Chang S, Ong SH. Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput Biol Med 2011; 41: 1–10.Web of ScienceCrossrefPubMedGoogle Scholar

  • [8]

    Ma B, Wu Y, Li P. Level set segmentation using image second order statistics. In: Zhang T, editor. Automatic target recognition and image analysis. Proceedings of SPIE, vol 8003, Nong Sang, Guilin, China 2011.Google Scholar

  • [9]

    Mallikarjuna PB, Guru DS. Performance Evaluation of Segmentation and Classification of Tobacco Seedling Diseases. Int J Mach Intell 2011; 3: 204–211.Google Scholar

  • [10]

    Mitchell IM. The flexible, extensible and efficient toolbox of level set methods. J Sci Comput 2008; 35: 300–329.Web of ScienceCrossrefGoogle Scholar

  • [11]

    Moraru L, Moldovanu S. Comparative study on performance of textural image features for active contour segmentation. Sci China Life Sci 2012; 55: 637–644.Web of ScienceCrossrefPubMedGoogle Scholar

  • [12]

    Mumford D, Shah J. Optimal approximation by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 1989; 42: 577–685.CrossrefGoogle Scholar

  • [13]

    Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulation. J Comput Phys 1988; 79: 12–49.CrossrefGoogle Scholar

  • [14]

    Paragios N. A level set approach for shape-driven segmentation and tracking of left ventricle. IEEE Trans Med Imaging 2003; 22: 773–776.CrossrefPubMedGoogle Scholar

  • [15]

    Saad NM, Abu-Bakar SAR, Abdullah AR, Salahuddin L, Muda S, Mokji M. Brain Lesion segmentation from diffusion weighted MRI based on adaptive thresholding and gray level co-occurrence matrix. Journal of Telecommunication, Electronic and Computer Engineering 2011; 3: 1–14.Google Scholar

  • [16]

    Schlaggar BL, Brown TT, Lugar HM, Visscher KM, Miezin FM, Petersen SE. Functional neuroanatomical differences between adults and school-age children in processing of single words. Science 2002; 296: 1476–1479.PubMedCrossrefGoogle Scholar

  • [17]

    Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. NeuroImage 2012; 59: 3774–3783.Web of ScienceCrossrefPubMedGoogle Scholar

  • [18]

    Shattuck DW, Prasad G, Mirza M, Narr KL, Toga AW. On line resource for validation of brain segmentation methods. NeuroImage 2009; 45: 431–439.CrossrefWeb of ScienceGoogle Scholar

  • [19]

    Suri JS, Liu K, Singh S, Laxminarayan SN, Zeng X, Reden L. Shape recovery algorithms using level sets in 2-D/3-Dmedical imagery: a state-of-the-art review. IEEE Trans Inf Technol Biomed 2002; 6: 8–28.CrossrefGoogle Scholar

  • [20]

    Wang L, He L, Mishra A, Li C. Active contours driven by local Gaussian distribution fitting energy. Signal Process 2009; 89: 2435–2447.CrossrefWeb of ScienceGoogle Scholar

  • [21]

    Wang L, Li C, Sun Q, Xia D, Kao C-Y. Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imaging Graph 2009; 33: 520–531.PubMedWeb of ScienceCrossrefGoogle Scholar

  • [22]

    Wang L, Shi F, Lin W, Gilmore JH, Shen D. Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 2011; 58: 805–817.PubMedCrossrefWeb of ScienceGoogle Scholar

  • [23]

    Zhang K, Zhang L, Song H, Zhou W. Active contours with selective local or global segmentation: a new formulation and level set method. Image Vision Comput 2010; 28: 668–676.Web of ScienceCrossrefGoogle Scholar

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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