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

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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.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings
EditorsJan Lellmann, Jan Modersitzki, Martin Burger
Number of pages12
PublisherSpringer
Publication date2019
Pages369-380
ISBN (Print)9783030223670
DOIs
Publication statusPublished - 2019
Event7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019 - Hofgeismar, Germany
Duration: 30 Jun 20194 Jul 2019

Conference

Conference7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019
LandGermany
ByHofgeismar
Periode30/06/201904/07/2019
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11603 LNCS
ISSN0302-9743

ID: 227228501