Level set segmentation with shape prior knowledge using intrinsic rotation, translation and scaling alignment

C. Arrieta, C. Sing-Long, S. Uribe, M. E. Andia, P. Irarrazaval, C. Tejos

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


Level set-based algorithms have been extensively used for medical image segmentation. Despite their relative success, standard level set segmentations tend to fail when images are severely corrupted or in poorly defined regions. This problem has been tackled incorporating shape prior knowledge, i.e. restricting the evolving curve to a distribution of shapes pre-defined during a training process. Such shape restriction needs to incorporate invariance to translation, rotations and scaling. The common approach for this is to solve a registration problem during the curve evolution, i.e. finding optimal registration parameters. This procedure is slow and produces variable results depending on the order in which the registration parameters were optimized. To overcome this issue Cremers et al. (2006) proposed an intrinsic alignment formulation, which is a normalized coordinate system for each shape, thus avoiding the optimization step to account for the registration. Nevertheless, their proposed solution considered only scaling and translation, but not rotations which are critical for medical imaging applications. We added rotations to this intrinsic alignment, using eigenvalues and eigenvector matrices of the covariance matrix of each shape, and we incorporated them into the evolution equation, allowing us to use shape priors in complex segmentation problems. We tested our algorithm combined with a Chan-Vese functional in synthetic images and in 2D right ventricle MRI.

Original languageEnglish
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
EditorsSebastien Ourselin, Jens Rittscher
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781479923748
Publication statusPublished - 21 Jul 2015
Externally publishedYes
EventIEEE International Symposium on Biomedical Imaging (ISBI) 2015 - New York, United States of America
Duration: 16 Apr 201519 Apr 2015
Conference number: 12th
https://ieeexplore.ieee.org/xpl/conhome/7150573/proceeding (Proceedings)

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


ConferenceIEEE International Symposium on Biomedical Imaging (ISBI) 2015
Abbreviated titleISBI 2015
Country/TerritoryUnited States of America
CityNew York
Internet address


  • Intrinsic Alignment
  • Level Set
  • Prior Knowledge
  • Segmentation

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