Abstract
A computer algorithm for fast identification and localization of structures of interest in images is presented. The algorithm is based on the analysis of a reduced set of image neighborhoods selected randomly by a constrained sampling of an associated image map of much smaller spatial resolution. The general approach is demonstrated by estimating the relative location of the breast tissue on a dataset of 860 digitized mammographic images. The computational times and breast tissue localization error rates are reported for different reduced spatial resolution image maps and three different features used for the corresponding neighborhood analysis. Our results show significant improvement on the error rates and computational times obtained with our approach compared to a pixel intensity thresholding approach. The algorithm implementation is very simple, requires less computation time than the sequential processing of each one of the image elements in a raster pattern and can be easily included into a hierarchical image analysis model.
Original language | English |
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Pages (from-to) | 270-279 |
Number of pages | 10 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 5034 |
DOIs | |
State | Published - 2003 |
Externally published | Yes |
Event | Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment - San Diego, CA, United States Duration: Feb 18 2003 → Feb 20 2003 |
Keywords
- Hierarchical models
- Image orientation
- Mammography
- Rapid scene analysis
- Segmentation