STFT-based denoising of elastograms

Optimal two-stage filtering of elastograms

“…The experimental outcomes indicate that the method can outperform the other schemes with respect to the achieved CNR e values, and also according to what visual inspection of the resulting elastograms suggests. It should be noted that each of the two modern approaches included in our comparisons had independently been shown [5], [6] to be superior to commonly used denoising approaches for the elastogram. Further experimentation involving clinical data will have to follow in order to validate this new approach and fully assess its advantages and limitations.…”

“…The FrFT-based denoising method [5] and the masked STFT scheme described in [6] were applied to the same noisy realization of the simulated elastogram that was used for all the presented results. The parameters required for the latter two methods were determined empirically as this was done in [5] and [6], respectively. For the given realization the FrFT-based method achieved a CNR e value of 65.69dB whereas the masked STFT approach resulted in a value of 60.60dB.…”

“…Alternative denoising methods such as those based on discrete wavelet transforms (WT) [3], [4], fractional Fourier transform (FrFT) [5] and short-time Fourier transform (STFT) [6] have been proposed for the accurate estimation of strain signals. Despite the preliminary nature of the above works, the presented results appeared promising.…”