caa a22 4500 467915067 CHVBK 20180406152859.0 cr unu---uuuuu 170328e20060601xx s 000 0 eng 10.1007/s11263-006-6658-x doi (NATIONALLICENCE)springer-10.1007/s11263-006-6658-x A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional [Elektronische Daten] [Xavier Bresson, Pierre Vandergheynst, Jean-Philippe Thiran] In this paper, we propose a new variational model to segment an object belonging to a given shape space using the active contour method, a geometric shape prior and the Mumford-Shah functional. The core of our model is an energy functional composed by three complementary terms. The first one is based on a shape model which constrains the active contour to get a shape of interest. The second term detects object boundaries from image gradients. And the third term drives globally the shape prior and the active contour towards a homogeneous intensity region. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variations and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We also prove the existence of this minimum in the space of functions with bounded variation. Applications of the proposed model are presented on synthetic and medical images. Springer Science + Business Media, LLC, 2006 image segmentation nationallicence variational model nationallicence active contour nationallicence level set method nationallicence shape prior nationallicence shape registration nationallicence principal components analysis nationallicence Mumford-Shah model nationallicence Bresson Xavier Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, Lausanne, CH-1015, Switzerland aut Vandergheynst Pierre Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, Lausanne, CH-1015, Switzerland aut Thiran Jean-Philippe Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, Lausanne, CH-1015, Switzerland aut International Journal of Computer Vision Kluwer Academic Publishers 68/2(2006-06-01), 145-162 0920-5691 68:2<145 2006 68 11263 https://doi.org/10.1007/s11263-006-6658-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-6658-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bresson Xavier Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, Lausanne, CH-1015, Switzerland aut NATIONALLICENCE 700 1- Vandergheynst Pierre Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, Lausanne, CH-1015, Switzerland aut NATIONALLICENCE 700 1- Thiran Jean-Philippe Signal Processing Institute, Swiss Federal Institute of Technology, EPFL-STI-ITS-Station 11, Lausanne, CH-1015, Switzerland aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 68/2(2006-06-01), 145-162 0920-5691 68:2<145 2006 68 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer