Fast constrained image segmentation using optimal spanning trees

Stanislav Harizanov, Svetozar Margenov, Ludmil Zikatanov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose a graph theoretical algorithm for image segmentation which preserves both the volume and the connectivity of the solid (non-void) phase of the image. The approach uses three stages. Each step optimizes the approximation error between the image intensity vector and piece-wise constant (indicator) vector characterizing the segmentation of the underlying image. The different norms in which this approximation can be measured give rise to different methods. The running time of our algorithm is O(N logN) for an image with N voxels.

Original languageEnglish (US)
Title of host publicationLarge-Scale Scientific Computing - 10th International Conference, LSSC 2015, Revised Selected Papers
EditorsIvan Lirkov, Svetozar D. Margenov, Jerzy Waśniewski
PublisherSpringer Verlag
Pages15-29
Number of pages15
ISBN (Print)9783319265193
DOIs
Publication statusPublished - Jan 1 2015
Event10th International Conference on Large-Scale Scientific Computing, LSSC 2015 - Sozopol, Bulgaria
Duration: Jun 8 2015Jun 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9374
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Large-Scale Scientific Computing, LSSC 2015
CountryBulgaria
CitySozopol
Period6/8/156/12/15

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Harizanov, S., Margenov, S., & Zikatanov, L. (2015). Fast constrained image segmentation using optimal spanning trees. In I. Lirkov, S. D. Margenov, & J. Waśniewski (Eds.), Large-Scale Scientific Computing - 10th International Conference, LSSC 2015, Revised Selected Papers (pp. 15-29). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9374). Springer Verlag. https://doi.org/10.1007/978-3-319-26520-9_2