Restoration of ultrasound images by nonlinear scale-space filtering

The quality of ultrasound images is limited by granular speckle noise. This paper presents two nonlinear restoration methods based on multiscale signal decomposition. Initially, signal-dependent multiplicative speckle noise is transformed to additive noise by a logarithm point operation. Rectangular coordinates are obtained by a polar coordinate transform of the sector image. The lateral distortions require filter masks that are locally adapted to the ellipsoidal speckle spots. The images are decomposed into frequency bands and morphological scales by a Laplacian pyramid and self-dual morphological filtering, respectively. In both cases the subbands are filtered by special rank-order/morphological filters depending on lateral and radial resolutions of the ultrasound image. In case of Laplacian subbands, multistage filters consider elongated structures by unidirectional median filtering and subsequent rank-order operations. Morphological scales contain size-dependent speckle-shaped objects and are filtered by a novel self-dual reconstruction operator that equally treats noise resulting from amplified and attenuated reflected sound-waves. The performance of the despeckle algorithms is demonstrated for different types of B-mode sector scans. Both methods show significant noise reduction capability preserving object contours due to nonlinear filtering of the subbands.