@Article{NMTMA-9-147,
author = {Vladimír Klement, Tomáš Oberhuber and Daniel Ševčovič},
title = {Application of the Level-Set Model with Constraints in Image Segmentation},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2016},
volume = {9},
number = {1},
pages = {147--168},
abstract = {<p style="text-align: justify;">We propose and analyze a constrained level-set method for semi-automatic
image segmentation. Our level-set model with constraints on the level-set function
enables us to specify which parts of the image lie inside respectively outside the
segmented objects. Such a-priori information can be expressed in terms of upper
and lower constraints prescribed for the level-set function. Constraints have the
same conceptual meaning as initial seeds of the popular graph-cuts based methods
for image segmentation. A numerical approximation scheme is based on the
complementary-finite volumes method combined with the Projected successive overrelaxation
method adopted for solving constrained linear complementarity problems.
The advantage of the constrained level-set method is demonstrated on several
artificial images as well as on cardiac MRI data.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.2015.m1418},
url = {http://global-sci.org/intro/article_detail/nmtma/12371.html}
}