Abstract
In this paper a novel multiresolution wavelet analysis (MWA) and non-stationary Gaussian Markov random field (GMRF) technique is introduced for the identification of microcalcifications with high accuracy. The hierarchical multiresolution wavelet information in conjunction with the contextual information of the images extracted from GMRF provides a highly efficient technique for microcalcification detection. A Bayesian learning paradigm realized via the expectation maximization (EM) algorithm was also introduced for edge detection or segmentation of larger lesions recorded on the mammograms. The effectiveness of the approach has been extensively tested with a number of mammographic images provided by a local hospital.
| Original language | English |
|---|---|
| Pages | 1737-1741 |
| Number of pages | 5 |
| Publication status | Published - 1996 |
| Event | Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3) - Anaheim, CA, USA Duration: 1996 Nov 2 → 1996 Nov 9 |
Other
| Other | Proceedings of the 1996 IEEE Nuclear Science Symposium. Part 1 (of 3) |
|---|---|
| City | Anaheim, CA, USA |
| Period | 96-11-02 → 96-11-09 |
All Science Journal Classification (ASJC) codes
- Radiation
- Nuclear and High Energy Physics
- Radiology Nuclear Medicine and imaging
