Title :
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
Author :
Lei, Tianhu ; Sewchand, Wilfred
Author_Institution :
Dept. of Radiat. Oncology, Maryland Univ., Baltimore, MD, USA
fDate :
3/1/1992 12:00:00 AM
Abstract :
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
Keywords :
computerised tomography; picture processing; statistical analysis; stochastic processes; Bayesian classifier; Gaussian random field; classification-maximization algorithm; expectation-maximization algorithm; finite normal mixture distribution; information theoretical criteria; model parameters; stochastic model-based image segmentation technique; Bayesian methods; Biomedical imaging; Computed tomography; Image analysis; Image segmentation; Image texture analysis; Optical imaging; Pixel; Stochastic processes; X-ray imaging;
Journal_Title :
Medical Imaging, IEEE Transactions on
