Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm.

Details

Serval ID
serval:BIB_26237840838D
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm.
Journal
Optics express
Author(s)
Castella C., Kinkel K., Descombes F., Eckstein M.P., Sottas P.E., Verdun F.R., Bochud F.O.
ISSN
1094-4087[electronic]
Publication state
Published
Issued date
2008
Peer-reviewed
Oui
Volume
16
Number
11
Pages
7595-75607
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't - Publication Status: ppublish
Abstract
Synthetic yet realistic images are valuable for many applications in visual sciences and medical imaging. Typically, investigators develop algorithms and adjust their parameters to generate images that are visually similar to real images. In this study, we used a genetic algorithm and an objective, statistical similarity measure to optimize a particular texture generation algorithm, the clustered lumpy backgrounds (CLB) technique, and synthesize images mimicking real mammograms textures. We combined this approach with psychophysical experiments involving the judgment of radiologists, who were asked to qualify the visual realism of the images. Both objective and psychophysical approaches show that the optimized versions are significantly more realistic than the previous CLB model. Anatomical structures are well reproduced, and arbitrary large databases of mammographic texture with visual and statistical realism can be generated. Potential applications include detection experiments, where large amounts of statistically traceable yet realistic images are needed.
Keywords
Algorithms, Artificial Intelligence, Breast Neoplasms/radiography, Cluster Analysis, Computer Simulation, Female, Humans, Imaging, Three-Dimensional/methods, Mammography/methods, Models, Genetic, Pattern Recognition, Automated/methods, Radiographic Image Enhancement/methods, Radiographic Image Interpretation, Computer-Assisted/methods, Reproducibility of Results, Sensitivity and Specificity
Pubmed
Web of science
Create date
06/01/2009 10:32
Last modification date
20/08/2019 13:04
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