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.

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基于轮廓波变换的光学相干断层心管图像图像去噪算法

光学相干断层扫描（OCT）正在成为生物医学领域日益重要的成像技术。然而，OCT的应用受到普遍存在的噪声的限制。在本研究中，OCT心管图像的噪声首先基于局部统计（即某个平坦区域的均值与标准差之间的线性关系）被验证为乘性的。噪声的方差在对数域中评估。在此基础上，构造联合概率密度函数，以考虑对数变换图像的轮廓波域中的方向间依赖性。然后，推导二元收缩函数，通过最大后验估计对图像进行去噪。通过主观评估和客观指标来合成图像、OCT心管图像和其他OCT组织图像进行系统比较实验。根据去噪结果以及该算法相对于小波算法的优势程度对实验结果进行了分析。结果表明，该算法提高了信噪比，同时保留了边缘，对于OCT心管图像等包含多方向信息的图像更具优势。