Framelet-based algorithm for segmentation of tubular structures

Xiaohao CAI*, Raymond H. CHAN, Serena MORIGI, Fiorella SGALLARI

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

8 Citations (Scopus)

Abstract

Framelets have been used successfully in various problems in image processing, including inpainting, impulse noise removal, super-resolution image restoration, etc. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation that depend on the partial differential equation modeling. In this paper, we apply the framelet-based approach to identify tube-like structures such as blood vessels in medical images. Our method iteratively refines a region that encloses the possible boundary or surface of the vessels. In each iteration, we apply the framelet-based algorithm to denoise and smooth the possible boundary and sharpen the region. Numerical experiments of real 2D/3D images demonstrate that the proposed method is very efficient and outperforms other existing methods.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision: Third International Conference, SSVM 2011, Revised Selected Papers
EditorsAlfred M. BRUCKSTEIN, Bart M. Haar ROMENY, Alexander M. BRONSTEIN, Michael M. BRONSTEIN
PublisherSpringer Berlin Heidelberg
Pages411-422
Number of pages12
ISBN (Electronic)9783642247859
ISBN (Print)9783642247842
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011 - Ein-Gedi, Israel
Duration: 29 May 20112 Jun 2011

Publication series

NameLecture Notes in Computer Science
Volume6667
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011
Country/TerritoryIsrael
CityEin-Gedi
Period29/05/112/06/11

Keywords

  • Magnetic Resonance Angiography
  • Binary Image
  • Active Contour
  • Tubular Structure
  • Image Restoration

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