TY  - JOUR
AU  - Kolev, K.
AU  - Kirchgeßner, N.
AU  - Houben, S.
AU  - Csiszar, A.
AU  - Rubner, W.
AU  - Palm, Ch.
AU  - Eiben, B.
AU  - Merkel, R.
AU  - Cremers, D.
TI  - A variational approach to vesicle membrane reconstruction from fluorescence imaging
JO  - Pattern recognition
VL  - 44
SN  - 0031-3203
CY  - Amsterdam
PB  - Elsevier
M1  - PreJuSER-16184
SP  - 2944 - 2958
PY  - 2011
N1  - Record converted from VDB: 12.11.2012
AB  - 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.
KW  - J (WoSType)
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000292947000011
DO  - DOI:10.1016/j.patcog.2011.04.019
UR  - https://juser.fz-juelich.de/record/16184
ER  -