Hyperbolic Wavelet-Fisz denoising for a model arising in Ultrasound Imaging

International audienceWe present an algorithm and its fully data-driven extension for noise reduction in ultrasound imaging. Our proposed method computes the hyperbolic wavelet transform of the image, before applying a multiscale variance stabilization technique, via a Fisz transformation. This adapts the wavelet coefficients statistics to the wavelet thresholding paradigm. The aim of the hyperbolic setting is to recover the image while respecting the anisotropic nature of structural details. The data-driven extension removes the need for any prior knowledge of the noise model parameters by estimating the noise variance using an isotonic Nadaraya-Watson estimator. Experiments on synthetic and real data, and comparisons with other noise reduction methods demonstrate the potential of our method at recovering ultrasound images while preserving tissue details. Finally, we emphasize the noise model we consider by applying our variance estimation procedure on real images

Image segmentation using a texture gradient based watershed transform

Abstract — The segmentation of images into meaningful and homogenous regions is a key method for image analy-sis within applications such as content based retrieval. The watershed transform is a well established tool for the seg-mentation of images. However, watershed segmentation is often not effective for textured image regions that are per-ceptually homogeneous. In order to properly segment such regions the concept of the “texture gradient ” is now intro-duced. Texture information and its gradient are extracted using a novel non-decimated form of a complex wavelet transform. A novel marker location algorithm is subse-quently used to locate significant homogeneous textured or non textured regions. A marker driven watershed transform is then used to properly segment the identified regions. The combined algorithm produces effective texture and intensity based segmentation for the application to content based im-age retrieval