A new feature-enhanced speckle reduction method based on multiscale analysis and synthesis for ultrasound B-mode imaging

Abstract

Effective speckle reduction in ultrasound B-mode imaging is important for improving image quality and the accuracy in image analysis. While multiscale analysis-based speckle reduction methods such as Laplacian pyramid nonlinear diffusion (LPND) and nonlinear multiscale wavelet diffusion (NMWD) showed enhanced speckle reduction, they suffer from excessive blurring and artificial appearance. In this paper, a new feature-enhanced speckle reduction (FESR) method based on multiscale analysis and feature enhancement filtering is presented for ultrasound B-mode imaging. To separate true clinical features (e.g., boundaries of lesions) from noise, the subband images from a Laplacian pyramid model are firstly generated. Then, a robust anisotropic diffusion process is applied to suppress the identified noise and the extracted features are selectively emphasized by suitable edge, coherence and contrast enhancement filtering from fine to coarse scales. The performance of the proposed FESR method was compared with the LPND and NMWD methods by measuring speckle's signal-to-noise ratio (SSNR) and contrast-to-noise ratio (CNR). With the FESR method, the mean SSNR value and the mean CNR value are significantly higher compared to the LPND and NMWD methods, i.e., 8.06±0.74 vs. 5.69±0.48, 7.14±0.93 and 6.89 ± 0.68 vs. 5.08 ± 0.33, 6.01 ± 0.53, respectively. These preliminary results demonstrates that the proposed FESR method can improve the image quality of ultrasound B-mode imaging by enhancing the visualization of borders and boundaries of lesions while effectively suppressing speckle.