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- W4310584533 abstract "Our related journal article presents a theoretical framework to relate the performance of photoacoustic-based computer vision systems to the parameters of the underlying photoacoustic imaging systems. However, software-based adjustments were not previously included. In this paper, we extend the previous framework to include software-based noise reduction algorithms (e.g., frame averaging). We derive expressions for generalized contrast-to-noise ratio (gCNR) predictions in frame-averaged photoacoustic images. These gCNR predictions are validated against histogram-based gCNR measurements, with mean absolute errors of 4.4×10 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>−3</sup> and 7.7×10 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>−3</sup> in the phantom and ex vivo environments, respectively. This extension reduced the mean absolute prediction error by 7.74% and 27.15% when compared to predictions with the previous photoacoustic framework applied to frame-averaged photoacoustic images from phantom and ex vivo data, respectively. Results demonstrate that our extended framework more accurately predicts target detectability in frame-averaged photoacoustic images, which is beneficial for photoacoustic-based computer vision systems." @default.
- W4310584533 created "2022-12-12" @default.
- W4310584533 creator A5021897202 @default.
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- W4310584533 date "2022-10-10" @default.
- W4310584533 modified "2023-09-27" @default.
- W4310584533 title "Predicting Generalized Contrast-to-Noise Ratios in Frame-Averaged Photoacoustic Images" @default.
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- W4310584533 doi "https://doi.org/10.1109/ius54386.2022.9958666" @default.
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