Numerous beamforming methods exist for ultrasound B-mode imaging, but it is known that adaptive/non-linear beamformers may alter the image dynamic range. To obtain an 8-bit image for further processing, it is necessary to determine a specific dynamic range, which may vary between beamforming methods in order to obtain a visually similar image. The aim here is to present an automated method to estimate the optimal dynamic range. We tested two phantom images and one in vivo image using six different beamforming techniques. The cumulative sums of the image histograms are compared with a standard dynamic range (i.e., 60 dB) and the contrast ratio and contrast-to-noise ratio are computed. We show that the automatically determined dynamic range is able to standardize the image among various beamforming techniques, which is essential when further image processing methods are employed.
Automatic dynamic range estimation for ultrasound image visualization and processing
Matrone G.
2020-01-01
Abstract
Numerous beamforming methods exist for ultrasound B-mode imaging, but it is known that adaptive/non-linear beamformers may alter the image dynamic range. To obtain an 8-bit image for further processing, it is necessary to determine a specific dynamic range, which may vary between beamforming methods in order to obtain a visually similar image. The aim here is to present an automated method to estimate the optimal dynamic range. We tested two phantom images and one in vivo image using six different beamforming techniques. The cumulative sums of the image histograms are compared with a standard dynamic range (i.e., 60 dB) and the contrast ratio and contrast-to-noise ratio are computed. We show that the automatically determined dynamic range is able to standardize the image among various beamforming techniques, which is essential when further image processing methods are employed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
