Speckle reduction is a crucial prerequisite of many computer-aided ultrasound diagnosis and treatment systems. However, most existing speckle reduction filters tend to concentrate the blurring near the features and introduce the hole artifacts, making the subsequent processing procedures complicated. Optimization-based methods can globally distribute such blurring, leading to better feature preservation. Motivated by this, we propose a novel optimization framework based on L0minimization for feature preserving ultrasound speckle reduction. We present an observation that the GAP, which integrates gradient and phase information, is extremely sparser in despeckled images (output) than in speckled images (input). Based on this observation, we propose an L0minimization framework to remove speckle noise and simultaneously preserve features in the ultrasound images. It seeks for the L0sparsity of the GAP values, and such sparsity is achieved by reducing small GAP values to zero in an iterative manner. Since features have larger GAP magnitudes than speckle noise, the proposed L0minimization is capable of effectively suppressing the speckle noise. Meanwhile, the rest of GAP values corresponding to prominent features are kept unchanged, leading to better preservation of those features. In addition, we propose an efficient and robust numerical scheme to transform the original intractable L0minimization into several sub-optimizations, from which we can quickly find their closed-form solutions. Experiments on synthetic and clinical ultrasound images demonstrate that our approach outperforms other state-of-the-art despeckling methods in terms of noise removal and feature preservation.
- Half-quadratic splitting method Iteratively re-weighted least squares framework
- L minimization 0
- Ultrasound speckle reduction
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence