Speckle noise reduction in US images: Enhanced filtering with optimal filter coefficients

Abstract

Ultrasound (US) imaging is one of the prominent techniques, which is widely used for its advantages such as affordability, non-radiation, and non-invasiveness. However, the visual evaluation in this imaging model was hampered by speckle noise. De-specking is therefore seen as the most important task. The preservation of every minute detail in the US images is the main challenge of de-speckling. This paper proposes a new de-speckling model that includes two major phases: the preprocessing stage and the noise reduction phase. Preprocessing is carried out by the anisotropic filtering process. In the noise reduction phase, two levels of decomposition take place. In the first level of decomposition, Discrete Waveform Transform (DWT) is used. The next level of decomposition (2-level) intakes the output from the first level, the sub-bands. The 2-level uses improved Weighted Guided Image Filtering (WGIF), and Gradient Domain Guided Image Filtering (GDGIF) for surpassing the noise. To make the process even more specific on efficient de-speckling, the concept of optimization is incorporated and eventually tunes the filter coefficients by considering Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR). This optimal tuning process is carried out by a new Shark Solution Integrated Coot Optimization Algorithm (SSI-COA). Finally, the noise-free image is produced through the inverse DWT. The efficiency of SSI-COA is evaluated over the conventional methods under various measures. The cost values using SSI-COA are less than (around 0.0518) when compared to other models. Moreover, a PSNR of 0.053275 is attained using SSI-COA for best-case scenarios.