One of the most widely used medical modality by healthcare industry is ultrasound imaging, which is often corrupted by multiplicative noise (known as speckle). The reduction of such kind of noise from ultrasound images is highly desirable for providing the proper diagnosis of a disease in real-time. The classical bilateral filter (CBF) is well known as most effective edge preserving and denoising filter for Gaussian noise reduction. Therefore, in this paper, a new speckle denoising filter is designed which is based on local statistics, Chi-square-based distance measure and box-based kernel function in bilateral filter framework for application and use in real time. The proposed speckle denoising scheme is tested on various synthetic, B-mode, simulated and real ultrasound images. The various quantitative and qualitative results suggest that the proposed local statistics-based bilateral filter (LSBF) outperforms the various existing speckle noise suppression techniques in term of denoising and restoration of fine textural information in the denoised images. The proposed LSBF method is compared with existing speckle noise reduction methods and experimental results demonstrate that, the proposed LSBF method have better noise removing and structure preserving capability as compared to existing standard denoising filters for speckle noise.

超声成像是医疗行业中使用最广泛的一种医疗方式，通常会因倍增噪声（称为斑点）而损坏。为了实时地正确诊断疾病，非常需要减少来自超声图像的这种噪声。传统的双边滤波器（CBF）是众所周知的用于降低高斯噪声的最有效的边缘保留和降噪滤波器。因此，本文设计了一种基于局部统计，基于卡方距离的度量和基于框的核函数的散斑去噪滤波器，以在双边滤波器框架中进行实时应用和使用。在各种合成，B模式，模拟和实际超声图像上测试了所提出的斑点去噪方案。各种定量和定性结果表明，在对降噪图像中的精细纹理信息进行去噪和恢复方面，所提出的基于局部统计的双边滤波器（LSBF）优于现有的各种散斑噪声抑制技术。将所提出的LSBF方法与现有的散斑噪声抑制方法进行了比较，实验结果表明，与现有的散斑噪声标准去噪滤波器相比，该方法具有更好的去噪和结构保持能力。