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
Artificial intelligenceExpectation–maximization algorithmMixture modelImage segmentationPattern recognition (psychology)Image processingIterative reconstructionComputer scienceGaussianSegmentationMathematicsComputer visionImage (mathematics)StatisticsMaximum likelihoodPhysics
Affiliated Institutions
Related Publications
A mixture of generalized hyperbolic distributions
Abstract We introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives wit...
Hierarchical Mixtures of Experts and the EM Algorithm
We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture co...
Inference on the Order of a Normal Mixture
Finite normal mixture models are used in a wide range of applications. Hypothesis testing on the order of the normal mixture is an important yet unsolved problem. Existing proce...
Image Segmentation in Video Sequences: A Probabilistic Approach
"Background subtraction" is an old technique for finding moving objects in a video sequence for example, cars driving on a freeway. The idea is that subtracting the current imag...
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-co...
Publication Info
- Year
- 1992
- Type
- article
- Volume
- 11
- Issue
- 1
- Pages
- 62-69
- Citations
- 91
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
91
OpenAlex
Cite This
T. Lei,
Wilfred Sewchand
(1992).
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.
IEEE Transactions on Medical Imaging
, 11
(1)
, 62-69.
https://doi.org/10.1109/42.126911
Identifiers
- DOI
- 10.1109/42.126911