@Article{EAJAM-7-1,
author = {},
title = {An Efficient Numerical Method for Mean Curvature-Based Image Registration Model},
journal = {East Asian Journal on Applied Mathematics},
year = {2017},
volume = {7},
number = {1},
pages = {125--142},
abstract = {<p style="text-align: justify;">Mean curvature-based image registration model firstly proposed by Chumchob-Chen-Brito
(2011) offered a better regularizer technique for both smooth and nonsmooth
deformation fields. However, it is extremely challenging to solve efficiently this
model and the existing methods are slow or become efficient only with strong assumptions
on the smoothing parameter β. In this paper, we take a different solution approach.
Firstly, we discretize the joint energy functional, following an idea of relaxed fixed point
is implemented and combine with Gauss-Newton scheme with Armijo’s Linear Search for
solving the discretized mean curvature model and further to combine with a multilevel
method to achieve fast convergence. Numerical experiments not only confirm that our
proposed method is efficient and stable, but also it can give more satisfying registration
results according to image quality.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.200816.031216a},
url = {https://global-sci.com/article/82692/an-efficient-numerical-method-for-mean-curvature-based-image-registration-model}
}