A Statistical Local Binary Fitting Model for Blood Vessel Segmentation

DOI

10.2991/iccia.2012.411How to use a DOI?

Keywords

segmentation, blood vessel, intensity inhomogeneity

Abstract

Network structure such as blood vessels in medical images are important features for computer-aided diagnosis and follow-up of many diseases. In this study, a new model-based segmentation method is proposed to detect blood vessels in medical images. The Local Binary Fitting(LBF) model with statistical distribution function is used for this purpose. The brain tissues and cerebral vessels in the image are modeled by Gaussian distribution and uniform distribution respectively. The region distribution combined with the LBF model is used in curve evolution. And the level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Comparisons with the LBF method show that our model can achieve better results.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shifeng Zhao
AU  - Mingquan Zhou
AU  - Kang Wang
PY  - 2014/05
DA  - 2014/05
TI  - A Statistical Local Binary Fitting Model for Blood Vessel Segmentation
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 1650
EP  - 1653
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.411
DO  - 10.2991/iccia.2012.411
ID  - Zhao2014/05
ER  -