Optimized generation of high resolution breast anthropomorphic software phantoms
- Computer and Information Sciences Department, Delaware State University, Dover, Delaware 19901 (United States)
Purpose: The authors present an efficient method for generating anthropomorphic software breast phantoms with high spatial resolution. Employing the same region growing principles as in their previous algorithm for breast anatomy simulation, the present method has been optimized for computational complexity to allow for fast generation of the large number of phantoms required in virtual clinical trials of breast imaging. Methods: The new breast anatomy simulation method performs a direct calculation of the Cooper's ligaments (i.e., the borders between simulated adipose compartments). The calculation corresponds to quadratic decision boundaries of a maximum a posteriori classifier. The method is multiscale due to the use of octree-based recursive partitioning of the phantom volume. The method also provides user-control of the thickness of the simulated Cooper's ligaments and skin. Results: Using the proposed method, the authors have generated phantoms with voxel size in the range of (25-1000 {mu}m){sup 3}/voxel. The power regression of the simulation time as a function of the reciprocal voxel size yielded a log-log slope of 1.95 (compared to a slope of 4.53 of our previous region growing algorithm). Conclusions: A new algorithm for computer simulation of breast anatomy has been proposed that allows for fast generation of high resolution anthropomorphic software phantoms.
- OSTI ID:
- 22098829
- Journal Information:
- Medical Physics, Vol. 39, Issue 4; Other Information: (c) 2012 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-2405
- Country of Publication:
- United States
- Language:
- English
Similar Records
A statistically defined anthropomorphic software breast phantom
Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data