A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation

Author

Pan, Zhigeng ; Lu, Jianfeng

Author_Institution

Hangzhou Dianzi Univ., Hangzhou

Volume

9

Issue

4

fYear

2007

Firstpage

32

Lastpage

38

Abstract

This paper discusses a new Bayesian-analysis-based region-growing algorithm for medical image segmentation that can robustly and effectively segment medical images. Specifically the approach studies homogeneity criterion parameters in a local neighbor region. Using the multislices Gaussian and anisotropic filters as a preprocess helps reduce an image´s noise. The algorithm framework is tested on CT and MRI image segmentation, and experimental results show that the approach is reliable and efficient.

Keywords

Bayes methods; biomedical MRI; computerised tomography; filtering theory; image segmentation; medical image processing; Bayes-based region-growing algorithm; Bayesian-analysis-based; CT; Gaussian filters; MRI; anisotropic filters; computerized tomography; magnetic resonance imaging; medical image segmentation; Anisotropic filters; Bayesian methods; Biomedical imaging; Computed tomography; Gaussian noise; Image segmentation; Magnetic resonance imaging; Noise reduction; Noise robustness; Testing; Bayesian analysis; anisotropic diffusion; image segmentation; region growing;

fLanguage

English

Journal_Title

Computing in Science & Engineering

Publisher

ieee

ISSN

1521-9615

Type

jour

DOI

10.1109/MCSE.2007.67

Filename

4263261