TY - GEN
T1 - Framelet-based algorithm for segmentation of tubular structures
AU - CAI, Xiaohao
AU - CHAN, Raymond H.
AU - MORIGI, Serena
AU - SGALLARI, Fiorella
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Magnetic Resonance Angiography
KW - Binary Image
KW - Active Contour
KW - Tubular Structure
KW - Image Restoration
UR - http://www.scopus.com/inward/record.url?scp=84855670714&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24785-9_35
DO - 10.1007/978-3-642-24785-9_35
M3 - Conference paper (refereed)
AN - SCOPUS:84855670714
SN - 9783642247842
T3 - Lecture Notes in Computer Science
SP - 411
EP - 422
BT - Scale Space and Variational Methods in Computer Vision: Third International Conference, SSVM 2011, Revised Selected Papers
A2 - BRUCKSTEIN, Alfred M.
A2 - ROMENY, Bart M. Haar
A2 - BRONSTEIN, Alexander M.
A2 - BRONSTEIN, Michael M.
PB - Springer Berlin Heidelberg
T2 - 3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011
Y2 - 29 May 2011 through 2 June 2011
ER -