Mammographic texture synthesis using genetic programming and clustered lumpy background

Cyril Castella; Karen Kinkel; François Descombes; Miguel P. Eckstein; Pierre-Edouard Sottas; Francis R. Verdun; François O. Bochud

doi:10.1117/12.650976

17 March 2006 Mammographic texture synthesis using genetic programming and clustered lumpy background

Cyril Castella, Karen Kinkel, François Descombes, Miguel P. Eckstein, Pierre-Edouard Sottas, Francis R. Verdun, François O. Bochud

Author Affiliations +

Proceedings Volume 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment; 61460U (2006) https://doi.org/10.1117/12.650976
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

In this work we investigated the digital synthesis of images which mimic real textures observed in mammograms. Such images could be produced in an unlimited number with tunable statistical properties in order to study human performance and model observer performance in perception experiments. We used the previously developed clustered lumpy background (CLB) technique and optimized its parameters with a genetic algorithm (GA). In order to maximize the realism of the textures, we combined the GA objective approach with psychophysical experiments involving the judgments of radiologists. Thirty-six statistical features were computed and averaged, over 1000 real mammograms regions of interest. The same features were measured for the synthetic textures, and the Mahalanobis distance was used to quantify the similarity of the features between the real and synthetic textures. The similarity, as measured by the Mahalanobis distance, was used as GA fitness function for evolving the free CLB parameters. In the psychophysical approach, experienced radiologists were asked to qualify the realism of synthetic images by considering typical structures that are expected to be found on real mammograms: glandular and fatty areas, and fiber crossings. Results show that CLB images found via optimization with GA are significantly closer to real mammograms than previously published images. Moreover, the psychophysical experiments confirm that all the above mentioned structures are reproduced well on the generated images. This means that we can generate an arbitrary large database of textures mimicking mammograms with traceable statistical properties.

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Cyril Castella, Karen Kinkel, François Descombes, Miguel P. Eckstein, Pierre-Edouard Sottas, Francis R. Verdun, and François O. Bochud "Mammographic texture synthesis using genetic programming and clustered lumpy background", Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 61460U (17 March 2006); https://doi.org/10.1117/12.650976

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