4.7 Article

Phase Asymmetry Ultrasound Despeckling With Fractional Anisotropic Diffusion and Total Variation

期刊

IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 29, 期 -, 页码 2845-2859

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2019.2953361

关键词

Image edge detection; Ultrasonic imaging; Speckle; Anisotropic magnetoresistance; Feature extraction; TV; Measurement; Ultrasound despeckling; speckle noise; fractional-order diffusion filter; fractional-order TV filter; edge detection; phase congruency; phase asymmetry; image denoising

资金

  1. National Natural Science Foundation of China [61271320]
  2. Medical Engineering Cross Fund of Shanghai Jiao Tong University [YG2014MS29]
  3. Translational Medicine Cross Fund of Shanghai Jiao Tong University [ZH2018ZDA19]

向作者/读者索取更多资源

We propose an ultrasound speckle filtering method for not only preserving various edge features but also filtering tissue-dependent complex speckle noises in ultrasound images. The key idea is to detect these various edges using a phase congruence-based edge significance measure called phase asymmetry (PAS), which is invariant to the intensity amplitude of edges and takes 0 in non-edge smooth regions and 1 at the idea step edge, while also taking intermediate values at slowly varying ramp edges. By leveraging the PAS metric in designing weighting coefficients to maintain a balance between fractional-order anisotropic diffusion and total variation (TV) filters in TV cost function, we propose a new fractional TV framework to not only achieve the best despeckling performance with ramp edge preservation but also reduce the staircase effect produced by integral-order filters. Then, we exploit the PAS metric in designing a new fractional-order diffusion coefficient to properly preserve low-contrast edges in diffusion filtering. Finally, different from fixed fractional-order diffusion filters, an adaptive fractional order is introduced based on the PAS metric to enhance various weak edges in the spatially transitional areas between objects. The proposed fractional TV model is minimized using the gradient descent method to obtain the final denoised image. The experimental results and real application of ultrasound breast image segmentation show that the proposed method outperforms other state-of-the-art ultrasound despeckling filters for both speckle reduction and feature preservation in terms of visual evaluation and quantitative indices. The best scores on feature similarity indices have achieved 0.867, 0.844 and 0.834 under three different levels of noise, while the best breast ultrasound segmentation accuracy in terms of the mean and median dice similarity coefficient are 96.25 and 96.15, respectively.

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