Medical ultrasound imaging

CT

MR

and X-ray imaging are four modern medical imaging techniques. Medical ultrasound imaging techniques are ultrasonic-based diagnostic imaging approaches used to visualize subcutaneous body structures

such as muscles

vessels

tendons

joints

and internal organs. Compared with other imaging techniques

medical ultrasound imaging is widely used in clinical diagnosis

especially in pregnant women and fetuses

because it is non-invasive

inexpensive

convenient

can be applied in real time

and so on. However

due to the influence of the ultrasonic imaging principle

the ultrasonic image is inevitably disturbed by speckle noise during the generation process

which not only reduces the quality of the ultrasonic image but also makes the identification and analysis of the image detail highly difficult. In this study

an improved non-local means (NLM) image denoising algorithm based on the noise model of the ultrasonic image is proposed. A statistical model of speckle noise is obtained based on the probability distribution of the ultrasonic image. Then

the Bayesian formula and speckle noise model are utilized to improve the weight function of the NLM filter algorithm. The weight function of the traditional NLM algorithm is based on Gaussian distribution

so it can suppress Gaussian noise well. However

it is unsuitable for speckle noise. In this study

the weight function is improved based on the speckle noise model to make the algorithm applicable to an ultrasonic image. The algorithm preprocesses the image according to the characteristics of the proposed weight function by using a pre-defined threshold. If the average gray value of the image is greater than 155

then the image is processed directly. If the average gray value of the image is less than 100

then the anti-colored image is used for denoising. If the image has an average gray value of 100 to 155

both the original and anti-colored images are processed

and the average of the results is calculated and used as the final result. This step makes the algorithm produce a good denoising effect. Afterward

different sampling intervals are utilized to subsample the image; each sampling interval must be smaller than the similar window size in the NLM algorithm. For each pixel in the sampled block

the filtered value is calculated with the improved NLM algorithm. If a pixel is in the intersection of two sampled blocks

the final estimated value of the pixel is calculated by the weighted average of the filtered values in the two sampled blocks. After all the pixels are calculated

de-speckling performances in terms of filtered time

peak signal-to-noise ratio (PSNR)

mean squared error (MSE)

and mean structural similarity (MSSIM) at different sampling intervals are analyzed to optimize the sampling interval so that the algorithm can reduce noise while reducing the processing time. Finally

the optimized sampling interval and the improved NLM algorithm are applied to ultrasonic image denoising. The search and image window sizes are fixed to 11×11 and 5×5

respectively

in the optimized Bayesian NLM algorithm (OBNLM) algorithm and the proposed algorithm. The optimal sampling interval is fixed to 3 according to the experimental results. Experiments on phantom images and real 2D ultrasound datasets show that the proposed algorithm outperforms other well-accepted methods

including the traditional NLM algorithm

OBNLM

non-local total variation (NLTV) algorithm

and speckle-reducing anisotropic diffusion filter (SRAD)

in terms of objective and subjective evaluations (e.g.

MSE

PSNR

MSSIM

and computational time). The images filtered with the proposed algorithm have a higher PSNR value than the other de-speckling algorithms

which means the proposed algorithm can preserve the details of the image information better

and the filtered image has similar edges as the noise-free image. Comparison of MSE and MSSIM values indicates that the proposed algorithm has lower MSE values and higher MSSIM values than the others

which means the proposed algorithm can better preserve the structure information of the original image. With regard to computation time

the proposed algorithm does not demonstrate superiority in this aspect

but the speed of the proposed algorithm is almost nine times faster than that of the traditional NLM algorithm. An experiment is also conducted on real 2D ultrasound images

and results show that the proposed algorithm provides a better visual effect than other well-accepted methods. Speckle noise reduces the quality of ultrasonic images and limits the development of automatic diagnostic technology. According to the speckle noise model of ultrasonic images

the weight function of the NLM algorithm is optimized to make the algorithm suitable for ultrasonic image denoising. Experimental results show that the proposed algorithm is better than other algorithms and suitable for ultrasonic image denoising.