Despeckling of ultrasound images using novel adaptive wavelet thresholding function

In the present work, a new thresholding function has been proposed for despeckling of ultrasound images. The main limitation of ultrasound images is presence of speckle noise which degrades image quality and hampers interpretation of the image. The proposed method has been first tested on the synthetic images so to analyse the performance of the proposed technique. The synthetic images are degraded by adding speckle noise with different degrees of noise variance (0.01–0.2) so as to analyse its performance for various noise variances. The proposed method is tested for orthogonal and biorthogonal wavelet filters. It is observed that Symlet 8 outperforms the other wavelet filters. The value of parameter ‘β’ is varied from 1 to 100 and its optimal value is selected which gives best results. Comparison with already existing exponential thresholding method, universal thresholding method, wiener filter and sparse coding have been made and proposed technique has given improved results. This method is tested on liver ultrasound images as well.

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