Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement.
Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement.
复制标题
用于超声图像增强的瑞利最大似然双边滤波器
DOI：
10.1186/s12938-017-0336-9
复制
发表时间：
2017-04-17
影响因子：
3.9
通讯作者：
Zhang Y
中科院分区：
文献类型：
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作者：
Li H;Wu J;Miao A;Yu P;Chen J;Zhang Y
背景超声成像由于其无创性和经济性，在计算机诊断中起着重要的作用。然而，超声图像在采集过程中不可避免地受到噪声和斑点噪声的污染。噪声和斑点直接影响医生对图像的判读，降低了临床诊断的准确性。去噪方法是提高超声图像质量的重要组成部分，然而，一些限制阻碍了结果，因为当前的去噪方法可以去除噪声，而忽略了斑点的统计特性，从而破坏了去斑点的有效性，反之亦然。此外，大多数现有算法在去除噪声或斑点之前不识别噪声、斑点或边缘，因此它们在模糊边缘细节的同时减少噪声和斑点。因此，这是一个具有挑战性的问题，为传统的方法，有效地去除噪声和斑点在超声图像中，同时保留边缘determins.MethodsTo克服上述局限性，一种新的方法，称为瑞利最大似然切换双边滤波器（RSBF），提出了两个步骤：噪声，斑点和边缘检测，然后滤波增强超声图像。首先，利用排序象限中值向量方案计算滤波窗口中的参考中值，与中心像素进行比较，以将目标像素分类为噪声、斑点或无噪声。随后，通过双边滤波器去除噪声，并通过瑞利最大似然滤波器抑制斑点，同时保持无噪声像素不变。为了定量评估所提出的方法的性能，合成的超声图像斑点污染的模拟使用的斑点模型，服从瑞利分布。然后，将原始图像与不同信噪比（SNR）水平的瑞利分布散斑相乘，再与高斯分布噪声相加，生成受损的合成图像。同时，利用临床乳腺超声图像对该方法的有效性进行了直观评价.要检查的性能，建议RSBF和六个国家的最先进的方法，超声斑点去除模拟超声图像与各种噪声和斑点level.ResultsThe建议RSBF的结果是令人满意的，因为高斯噪声和瑞利斑点大大抑制之间的比较测试。该方法可以提高信噪比的增强图像的近15和13 dB相比，被斑点污染的图像，以及图像污染的斑点和噪声在不同的信噪比水平下，分别。RSBF在增强临床超声图像边缘的同时，有效地平滑了斑点和噪声。在对比实验中，该方法证明了其优越性的准确性和鲁棒性去噪和边缘保护下的各种水平的噪声和斑点的视觉质量，以及数值指标，如峰值信噪比，信噪比和均方根误差conclusionsThe实验结果表明，该方法是有效的去除斑点和背景噪声的超声图像。主要原因是它执行“检测和替换”两步机制。所提出的RBSF的优点在于两个方面。该方法首先根据中心像素的绝对灰度值将其分为噪声、斑点和无噪声纹理。
BackgroundUltrasound imaging plays an important role in computer diagnosis since it is non-invasive and cost-effective. However, ultrasound images are inevitably contaminated by noise and speckle during acquisition. Noise and speckle directly impact the physician to interpret the images and decrease the accuracy in clinical diagnosis. Denoising method is an important component to enhance the quality of ultrasound images; however, several limitations discourage the results because current denoising methods can remove noise while ignoring the statistical characteristics of speckle and thus undermining the effectiveness of despeckling, or vice versa. In addition, most existing algorithms do not identify noise, speckle or edge before removing noise or speckle, and thus they reduce noise and speckle while blurring edge details. Therefore, it is a challenging issue for the traditional methods to effectively remove noise and speckle in ultrasound images while preserving edge details.MethodsTo overcome the above-mentioned limitations, a novel method, called Rayleigh-maximum-likelihood switching bilateral filter (RSBF) is proposed to enhance ultrasound images by two steps: noise, speckle and edge detection followed by filtering. Firstly, a sorted quadrant median vector scheme is utilized to calculate the reference median in a filtering window in comparison with the central pixel to classify the target pixel as noise, speckle or noise-free. Subsequently, the noise is removed by a bilateral filter and the speckle is suppressed by a Rayleigh-maximum-likelihood filter while the noise-free pixels are kept unchanged. To quantitatively evaluate the performance of the proposed method, synthetic ultrasound images contaminated by speckle are simulated by using the speckle model that is subjected to Rayleigh distribution. Thereafter, the corrupted synthetic images are generated by the original image multiplied with the Rayleigh distributed speckle of various signal to noise ratio (SNR) levels and added with Gaussian distributed noise. Meanwhile clinical breast ultrasound images are used to visually evaluate the effectiveness of the method. To examine the performance, comparison tests between the proposed RSBF and six state-of-the-art methods for ultrasound speckle removal are performed on simulated ultrasound images with various noise and speckle levels.ResultsThe results of the proposed RSBF are satisfying since the Gaussian noise and the Rayleigh speckle are greatly suppressed. The proposed method can improve the SNRs of the enhanced images to nearly 15 and 13 dB compared with images corrupted by speckle as well as images contaminated by speckle and noise under various SNR levels, respectively. The RSBF is effective in enhancing edge while smoothing the speckle and noise in clinical ultrasound images. In the comparison experiments, the proposed method demonstrates its superiority in accuracy and robustness for denoising and edge preserving under various levels of noise and speckle in terms of visual quality as well as numeric metrics, such as peak signal to noise ratio, SNR and root mean squared error.ConclusionsThe experimental results show that the proposed method is effective for removing the speckle and the background noise in ultrasound images. The main reason is that it performs a “detect and replace” two-step mechanism. The advantages of the proposed RBSF lie in two aspects. Firstly, each central pixel is classified as noise, speckle or noise-free texture according to the absolute …