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

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

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 aniso...

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Veröffentlicht in:Computing in science & engineering 2007-07, Vol.9 (4), p.32-38
Hauptverfasser: Pan, Zhigeng, Lu, Jianfeng
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
anisotropic diffusion
Anisotropic filters
Bayesian analysis
Bayesian methods
Biomedical imaging
Computed tomography
Gaussian
Gaussian noise
Homogeneity
Image segmentation
Magnetic resonance imaging
Medical
Noise reduction
Noise robustness
region growing
Segments
Testing
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Beschreibung
Zusammenfassung: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.
ISSN:1521-9615
1558-366X
DOI:10.1109/MCSE.2007.67