Statistical approach to X-ray CT imaging and its applications in image analysis. II. A new stochastic model-based image segmentation technique for X-ray CT image
| Autor: | W. Sewchand, T. Lei |
|---|---|
| Rok vydání: | 1992 |
| Předmět: |
Radiological and Ultrasound Technology
Pixel Segmentation-based object categorization business.industry Image registration Scale-space segmentation Image segmentation Computer Science Applications Computer Science::Computer Vision and Pattern Recognition Expectation–maximization algorithm Medical imaging Computer vision Tomography Artificial intelligence Electrical and Electronic Engineering business Software Mathematics |
| Zdroj: | IEEE Transactions on Medical Imaging. 11:62-69 |
| ISSN: | 0278-0062 |
| Popis: | 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. > |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|