Referenced Items are not available for this document.
For pt.I, see ibid., vol.11, no.1, p.53.61 (1992). Based on the statistical properties of X-ray CT imaging given in pt.I, an unsupervised stochastic model-based image segmentation technique for X-ray CT images is presented. This technique utilizes the finite normal mixture distribution and the underlying Gaussian random field (GRF) as the stochastic image model. The number of image classes in the observed image is detected by information theoretical criteria (AIC or MDL). The parameters of the model are estimated by expectation-maximization (EM) and classification-maximization (CM) algorithms. Image segmentation is performed by a Bayesian classifier. Results from the use of simulated and real X-ray computerized tomography (CT) image data are presented to demonstrate the promise and effectiveness of the proposed technique
Published in:
Medical Imaging, IEEE Transactions on
(Volume:11
,
Issue:
1
)
Date of Publication: Mar 1992
- Page(s):
- 62 - 69
- ISSN :
- 0278-0062
- INSPEC Accession Number:
- 4165592
- Digital Object Identifier :
- 10.1109/42.126911
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Mar 1992
- Sponsored by :
- IEEE Engineering in Medicine and Biology Society
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