Mr. Jianbin Feng Profile

Jianbin Feng

at Huazhong Univ of Science and Technology

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Proceedings Article | 21 March 2014 Paper

Non-local total variation method for despeckling of ultrasound images

Jianbin Feng, Mingyue Ding, Xuming Zhang

Proceedings Volume 9034, 90342F (2014) https://doi.org/10.1117/12.2043505

KEYWORDS: Ultrasonography, Speckle, Image processing, Denoising, Signal to noise ratio, Visualization, Medical imaging, Medical research, Lymphatic system, Liver

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Despeckling of ultrasound images, as a very active topic research in medical image processing, plays an important or even indispensable role in subsequent ultrasound image processing. The non-local total variation (NLTV) method has been widely applied to denoising images corrupted by Gaussian noise, but it cannot provide satisfactory restoration results for ultrasound images corrupted by speckle noise. To address this problem, a novel non-local total variation despeckling method is proposed for speckle reduction. In the proposed method, the non-local gradient is computed on the images restored by the optimized Bayesian non-local means (OBNLM) method and it is introduced into the total variation method to suppress speckle in ultrasound images. Comparisons of the restoration performance are made among the proposed method and such state-of-the-art despeckling methods as the squeeze box filter (SBF), the non-local means (NLM) method and the OBNLM method. The quantitative comparisons based on synthetic speckled images show that the proposed method can provide higher Peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) than compared despeckling methods. The subjective visual comparisons based on synthetic and real ultrasound images demonstrate that the proposed method outperforms other compared algorithms in that it can achieve better performance of noise reduction, artifact avoidance, edge and texture preservation.

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