Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers

TY - GEN

T1 - Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers

AU - Hansen, Jacob Daniel Kirstejn

AU - Lauze, François

PY - 2019

Y1 - 2019

N2 - This paper presents a 2D and 3D variational segmentation approach based on a similarity invariant, i.e., translation, scaling, and rotation invariant shape regulariser. Indeed, shape moments of order up to 2 for shapes with limited symmetries can be combined to provide a shape normalisation for the group of similarities. In order to obtain a segmentation objective function, a two-means or two-local-means data term is added to it. Segmentation is then obtained by standard gradient descent on it. We demonstrate the capabilities of the approach on a series of experiments, of different complexity levels. We specifically target rat brain shapes in MR scans, where the setting is complex, because of bias field and complex anatomical structures. Our last experiments show that our approach is indeed capable of recovering brain shapes automatically.

AB - This paper presents a 2D and 3D variational segmentation approach based on a similarity invariant, i.e., translation, scaling, and rotation invariant shape regulariser. Indeed, shape moments of order up to 2 for shapes with limited symmetries can be combined to provide a shape normalisation for the group of similarities. In order to obtain a segmentation objective function, a two-means or two-local-means data term is added to it. Segmentation is then obtained by standard gradient descent on it. We demonstrate the capabilities of the approach on a series of experiments, of different complexity levels. We specifically target rat brain shapes in MR scans, where the setting is complex, because of bias field and complex anatomical structures. Our last experiments show that our approach is indeed capable of recovering brain shapes automatically.

UR - http://www.scopus.com/inward/record.url?scp=85068482213&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-22368-7_29

DO - 10.1007/978-3-030-22368-7_29

M3 - Article in proceedings

AN - SCOPUS:85068482213

SN - 9783030223670

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 369

EP - 380

BT - Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings

A2 - Lellmann, Jan

A2 - Modersitzki, Jan

A2 - Burger, Martin

PB - Springer

T2 - 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019

Y2 - 30 June 2019 through 4 July 2019

ER -