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首页> 外文期刊>Image Processing, IET >Image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image
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Image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image

机译：基于Contourlet变换的光学相干断层成像心管图像降噪算法。

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摘要

Optical coherence tomography (OCT) is becoming an increasingly important imaging technology in the Biomedical field. However, the application of OCT is limited by the ubiquitous noise. In this study, the noise of OCT heart tube image is first verified as being multiplicative based on the local statistics (i.e. the linear relationship between the mean and the standard deviation of certain flat area). The variance of the noise is evaluated in log-domain. Based on these, a joint probability density function is constructed to take the inter-direction dependency in the contourlet domain from the logarithmic transformed image into account. Then, a bivariate shrinkage function is derived to denoise the image by the maximum a posteriori estimation. Systemic comparative experiments are made to synthesis images, OCT heart tube images and other OCT tissue images by subjective assessment and objective metrics. The experiment results are analysed based on the denoising results and the predominance degree of the proposed algorithm with respect to the wavelet-based algorithm. The results show that the proposed algorithm improves the signal-to-noise ratio, whereas preserving the edges and has more advantages on the images containing multi-direction information like OCT heart tube image.
机译：光学相干断层扫描（OCT）成为生物医学领域中越来越重要的成像技术。但是，OCT的应用受到普遍存在的噪声的限制。在这项研究中，首先根据本地统计数据（即某些平坦区域的平均值和标准偏差之间的线性关系）验证了OCT心管图像的噪声是否具有可乘性。在对数域中评估噪声的方差。基于这些，构造联合概率密度函数，以考虑对数变换图像在轮廓波域中的方向间相关性。然后，导出二元收缩函数以通过最大后验估计对图像进行降噪。通过主观评估和客观指标对系统图像，OCT心管图像和其他OCT组织图像进行系统的比较实验。基于去噪结果和所提算法相对于基于小波的算法的优势程度，对实验结果进行了分析。结果表明，所提出的算法提高了信噪比，同时保留了边缘并在包含多方向信息的图像（如OCT心管图像）上具有更大的优势。

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  • 来源
    《Image Processing, IET》 |2013年第5期|442-450|共9页
  • 作者

    Guo Q.; Dong F.; Sun S.; Lei B.;

  • 作者单位

    Institute of Intelligent Vision and Image Information, China Three Gorges University, Yichang, Hubei 443002, People's Republic of China|c|;

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