Speckle reduction and deblurring of ultrasound images using artificial neural network

Ultrasound (US) imaging is widely used in clinical diagnostics as it is an economical, portable, painless, comparatively safe, and non-invasive real-time tool. However, the image quality of US imaging is severely affected by the presence of speckle noise during the acquisition process. It is essential to achieve speckle-free high resolution US imaging for better clinical diagnosis. In this paper, we propose a speckle and blur reduction algorithm for US imaging based on artificial neural networks (ANNs). Here, speckle noise is modelled as a multiplicative noise following a Rayleigh distribution, whereas blur is modelled as a Gaussian blur function. The noise and blur variances are estimated by a cascade-forward back propagation (CFBP) neural network using a set of intensity and wavelet features of the US image. The estimated noise and blur variances are then used for speckle reduction by solving the inverse Rayleigh function, and for de-blurring, using the Lucy-Richardson algorithm. The proposed approach gives improved results for both qualitative and quantitative measures.