Deconvolutional speckle reducing anisotropic diffusion

In order to propel the analysis of medical ultrasound imagery from qualitative observation to quantitative measurement, the obstacles of distortion from speckle and from blurring due to the point spread function must be overcome. A recent partial differential equation (PDE) based enhancement technique has improved the ability to segment ultrasound images and to detect salient edges. However, this diffusion method often distorts the size of image features and may in fact efface subtle features. This paper proposes a new PDE that combines the enhancement of speckle reducing anisotropic diffusion (SRAD) with the mechanism of deconvolution. The resulting method, called deconvolutional speckle reducing anisotropic diffusion (DeSpeRADo), surpasses the edge localization ability of SRAD while yielding lower error in terms of area estimation and improved detection of fine features. A comparative study employs 100 experiments to contrast the quantification enabled by adaptive filtering, inverse filtering, diffusion and the new DeSpeRADo technique.