The percentage of radiodense tissue in the breast has been shown to be a reliable marker for breast cancer risk. In this paper, we present an image processing technique for estimating radiodense tissue in digitized mammograms. First, the mammogram is segmented into tissue and nontissue regions. This segmentation process involves the generation of a segmentation mask that is developed using a radial basis function neural network. Subsequently, the image is processed for estimating the amount of radiodense tissue. The estimation process involves the generation of a modified Neyman-Pearson threshold to segment the radiodense and radiolucent tissue. Typical research results are presented - these have been independently validated by a radiologist.
|Original language||English (US)|
|Number of pages||2|
|Journal||Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings|
|State||Published - Dec 1 2002|
|Event||Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States|
Duration: Oct 23 2002 → Oct 26 2002
All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
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