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Updated: May 25, 2026
Tissue-simulating Phantoms for Assessing Potential Near-infrared Fluorescence Imaging Applications in Breast Cancer Surgery
Published on: September 19, 2014
Improving textural realism in breast phantom images
Luana de M Omena1, Guilherme M de Oliveira1, Thaís G do Rêgo1
1Center of Informatics, Federal University of Paraíba, Rua dos Escoteiros s/n, João Pessoa, Paraíba, Brazil.
Background:
Breast phantom images can lack textural realism, limiting their utility for imaging research. Achieving realism across all BI-RADS breast densities is essential for representative, unbiased datasets.
Purpose:
This study validated simplex noise for improving realism and density fidelity in simulated mammary parenchyma across BI-RADS categories.
Methods:
Two reader trials were conducted with eight students from the Artificial Intelligence Applications Laboratory (ARIA) at the Federal University of Paraíba, Brazil. In the first trial, participants compared paired images with and without simplex noise for overall realism in BI-RADS A (fatty) and BI-RADS D (dense) patterns using 2-AFC. During the second trial, optimized parameters were tested across all four BI-RADS categories. Readers selected the most realistic image in each pair and rated confidence levels.
Results:
In Trial 1, the simplex enhanced reconstruction was judged as more realistic in 96% of BI-RADS D evaluations, compared to 58.6% for BI-RADS A. Trial 2 achieved a high realism consensus for simplex enhanced reconstructions (1,288 vs. 312 selections), with category improvements: BI-RADS A (92.5%), B (92.25%), C (81%), and D (57.75%). ROC analysis confirmed high discriminative performance, with AUC values of 0.95 (A), 0.74 (B), 0.85 (C), and 0.93 (D). Results indicate density dependent effects, with more pronounced improvements in lower density categories.
Conclusions:
The introduction of simplex noise enhances the textural realism of simulated mammographic images, particularly representing variability across BI-RADS categories. These simulations provide a resource for testing and validation without patient data.
