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)


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
Number of pages12
ISBN (Electronic)9783642247859
ISBN (Print)9783642247842
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
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011


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


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