An improved model for joint segmentation and registration based on linear curvature smoother

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Ibrahim, Mazlinda and Chen, Ke and Rada, Lavdie (2016) An improved model for joint segmentation and registration based on linear curvature smoother. Journal of Algorithms and Computational Technology, 10 (4). pp. 314-324. ISSN 1748-3026 (https://doi.org/10.1177/1748301816668027)

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Abstract

Image segmentation and registration are two of the most challenging tasks in medical imaging. They are closely related because both tasks are often required simultaneously. In this article, we present an improved variational model for a joint segmentation and registration based on active contour without edges and the linear curvature model. The proposed model allows large deformation to occur by solving in this way the difficulties other jointly performed segmentation and registration models have in case of encountering multiple objects into an image or their highly dependence on the initialisation or the need for a pre-registration step, which has an impact on the segmentation results. Through different numerical results, we show that the proposed model gives correct registration results when there are different features inside the object to be segmented or features that have clear boundaries but without fine details in which the old model would not be able to cope.

ORCID iDs

Ibrahim, Mazlinda, Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623 and Rada, Lavdie;
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