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Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images with Applications to Computer Vision
The successful integration of computer vision, robotic actuation, and photoacoustic imaging to find and follow targets of interest during surgical and interventional procedures requires accurate photoacoustic target detectability. This detectability has traditionally been assessed with image quality metrics such as contrast, contrast-to-noise ratio, and signal-to-noise ratio (SNR). However, predicting target tracking performance expectations when using these traditional metrics is difficult duedoi:10.1109/tuffc.2022.3169082 pmid:35446763 fatcat:ldifdxet4bgujbnjgvccf7onzu