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@ARTICLE{Kolev:16184,
      author       = {Kolev, K. and Kirchgeßner, N. and Houben, S. and Csiszar,
                      A. and Rubner, W. and Palm, Ch. and Eiben, B. and Merkel, R.
                      and Cremers, D.},
      title        = {{A} variational approach to vesicle membrane reconstruction
                      from fluorescence imaging},
      journal      = {Pattern recognition},
      volume       = {44},
      issn         = {0031-3203},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {PreJuSER-16184},
      pages        = {2944 - 2958},
      year         = {2011},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {Biological applications like vesicle membrane analysis
                      involve the precise segmentation of 3D structures in noisy
                      volumetric data, obtained by techniques like magnetic
                      resonance imaging (MRI) or laser scanning microscopy (LSM).
                      Dealing with such data is a challenging task and requires
                      robust and accurate segmentation methods. In this article,
                      we propose a novel energy model for 3D segmentation fusing
                      various cues like regional intensity subdivision, edge
                      alignment and orientation information. The uniqueness of the
                      approach consists in the definition of a new anisotropic
                      regularizer, which accounts for the unbalanced slicing of
                      the measured volume data, and the generalization of an
                      efficient numerical scheme for solving the arising
                      minimization problem, based on linearization and fixed-point
                      iteration. We show how the proposed energy model can be
                      optimized globally by making use of recent continuous convex
                      relaxation techniques. The accuracy and robustness of the
                      presented approach are demonstrated by evaluating it on
                      multiple real data sets and comparing it to alternative
                      segmentation methods based on level sets. Although the
                      proposed model is designed with focus on the particular
                      application at hand, it is general enough to be applied to a
                      variety of different segmentation tasks. (C) 2011 Elsevier
                      Ltd. All rights reserved.},
      keywords     = {J (WoSType)},
      cin          = {ICS-7},
      ddc          = {000},
      cid          = {I:(DE-Juel1)ICS-7-20110106},
      pnm          = {BioSoft: Makromolekulare Systeme und biologische
                      Informationsverarbeitung (FUEK505) / 89572 - (Dys-)function
                      and Plasticity (POF2-89572)},
      pid          = {G:(DE-Juel1)FUEK505 / G:(DE-HGF)POF2-89572},
      shelfmark    = {Computer Science, Artificial Intelligence / Engineering,
                      Electrical $\&$ Electronic},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000292947000011},
      doi          = {10.1016/j.patcog.2011.04.019},
      url          = {https://juser.fz-juelich.de/record/16184},
}