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摘要:
光学相干层析扫描(OCT)作为一种新型无创高分辨率扫描方式，在临床上得到广泛应用，但是OCT图像本身存在严重的散斑噪声，这大大影响了疾病的诊断。本文针对OCT图像中的乘性散斑噪声，改进了两种原始字典降噪算法。该算法首先对OCT图像进行对数变换，采用正交匹配追踪算法进行稀疏编码，以及K奇异值分解学习算法进行自适应字典的更新，最后通过加权平均以及指数变换回到空域。实验结果表明，本文改进的两种字典算法能有效降低OCT图像中的散斑噪声，获得良好的视觉效果。并通过均方误差(MSE)、峰值信噪比(PSNR)、结构相似性(SSIM)以及边缘保持指数(EPI)四个指标评价降噪效果，与两种原始字典降噪算法和传统滤波算法相比，两种改进字典算法降噪效果优于其他算法，其中自适应字典算法表现更好。
Abstract:As a new non-invasive and high-resolution scanning method, optical coherence tomography (OCT) has been widely used in clinical practice, but OCT images have serious speckle noise, which greatly affects the diagnosis of diseases. Two original dictionary noise reduction algorithms have been improved for multiplicative speckle noise in OCT. The algorithm first performs logarithmic transformation on OCT images, uses orthogonal matching pursuit algorithm for sparse coding, and K singular value decomposition learning algorithm to update adaptive dictionary. Finally, it returns to the space domain through weighted average and exponential transformation. The experimental results show that the improved two dictionary algorithms can effectively reduce the speckle noise in OCT images and obtain good visual effects. The noise reduction effect is evaluated by four factors: mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and edge-preserving index (EPI). Compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the noise reduction effect of the two improved dictionary algorithms is better than that of other algorithms, and the improved adaptive dictionary algorithm performs better.
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Overview: As a new non-invasive high-resolution scanning method, optical coherence tomography (OCT) has been widely used in clinical practice. Since the OCT imaging system uses an interference technique, the use of tissue scattering properties of light will inevitably introduce speckle noise. These speckle noises reduce the signal-to-noise ratio and contrast of the image, and also destroy the edge features of the image. As a result, it seriously affects people's accurate acquisition of image information. Therefore, the processing of OCT image speckle noise is very important before making a clinical diagnosis. The dictionary algorithm was originally proposed for Gaussian additive noise. This paper improves two original dictionary noise reduction algorithms for multiplicative speckle noise in OCT. The improved algorithm is divided into four steps. The first step is to establish and solve the speckle noise model of the OCT image. Firstly, the sparse domain model of small-sized image blocks is established and its noise reduction problem is solved. Then, the ideas in the Markov random field are used to generalize to large-size images. In the second step, logarithmically transforming the OCT image and performing noise estimation; In the third step, overlapping blocks the noisy image, the size of the image block is 8 pixels×8 pixels, the dictionary algorithm requires sparse coding and noise reduction for each block. The orthogonal matching pursuit algorithm (OMP) is used to perform sparse coding of two dictionary algorithms. In the fixed dictionary algorithm, the dictionary selects the discrete cosine transform (DCT) dictionary. In the adaptive dictionary algorithm, the initial dictionary selects the DCT dictionary and the dictionary training is performed by itself, and the dictionary update is completed by the K singular value decomposition learning algorithm; In the fourth step, the overlapping image blocks in the sparse coding stage are weighted averaged and returned to the spatial domain by exponential transformation. Selecting a randomly OCT slice and reduce noise for it, compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the improved two dictionary algorithms preserve most of the image information and edge detail information while suppressing speckle noise. Furthermore, three random OCT slice images are selected to simulate the improved two dictionary denoising algorithms. The improved adaptive dictionary algorithm has better noise reduction performance through subjective visual effects and four objective evaluation indicators. The two improved dictionary noise reduction algorithms proposed in this paper can be flexibly applied to various OCT noisy images and serve for subsequent image processing.
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图 1 改进字典算法降噪流程图
Figure 1. The noise reduction flowchart of improved dictionary algorithm
图 2 训练字典。(a)固定字典；(b)自适应字典
Figure 2. Trained dictionary.(a) Fixed dictionary; (b) Adaptive dictionary
图 3 各算法的降噪结果。(a) OCT原图；(b)原固定字典算法；(c)原自适应字典算法；(d)中值滤波；(e) Lee滤波；(f)维纳滤波；(g)改进固定字典算法；(h)改进自适应字典算法
Figure 3. Speckle reduction results of seven algorithms.(a) Original OCT image; (b) Original fixed dictionary algorithm; (c) Original adaptive dictionary algorithm; (d) Median filtering; (e) Lee filtering; (f) Wiener filtering; (g) Improved fixed dictionary algorithm; (h) Improved adaptive dictionary algorithm
图 4 两种改进后字典算法的降噪结果图。(ac)随机选取的三张OCT切片；(df)改进固定字典降噪图；(gi)改进自适应字典降噪图
Figure 4. Speckle reduction results of two improved dictionary algorithms.(ac) Randomly selected three OCT slices; (df) Noise reduction results by improved fixed dictionary algorithms; (gi) Noise reduction results by improved adaptive dictionary algorithms
表 1 七种降噪算法的性能对比
Table 1. Performance comparison of seven algorithms
MSE PSNR/dB SSIM EPI OFD 85.0727 28.8329 0.5037 0.4345 OAD 70.4629 29.6512 0.5137 0.4387 中值 97.0321 28.2617 0.6643 0.3464 Lee 101.0175 28.0868 0.7591 0.5692 维纳 72.5142 29.5266 0.6896 0.3438 IFD 69.5172 29.7099 0.8057 0.6842 IAD 46.8668 31.4222 0.7988 0.6569 
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表 2 两种改进的字典算法的降噪结果值
Table 2. Speckle reduction result values of two improved dictionary algorithms
MSE PSNR/dB SSIM EPI 切片97 IFD 47.2845 31.3836 0.8388 0.5962 IAD 42.1495 31.8829 0.8540 0.6051 切片41 IFD 53.8202 30.8213 0.8299 0.5852 IAD 45.1312 31.5860 0.8480 0.6150 切片26 IFD 46.4219 31.4636 0.8306 0.5882 IAD 45.5145 32.0116 0.8468 0.6004 平均值 IFD 49.1755 31.2228 0.8331 0.5899 IAD 42.7332 31.8268 0.8496 0.6068
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