Ayana Gelan, Dese Kokeb, Raj Hakkins, Krishnamoorthy Janarthanan, Kwa Timothy
Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea.
School of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia.
Diagnostics (Basel). 2022 Mar 30;12(4):862. doi: 10.3390/diagnostics12040862.
The ultrasonic technique is an indispensable imaging modality for diagnosis of breast cancer in young women due to its ability in efficiently capturing the tissue properties, and decreasing nega-tive recognition rate thereby avoiding non-essential biopsies. Despite the advantages, ultrasound images are affected by speckle noise, generating fine-false structures that decrease the contrast of the images and diminish the actual boundaries of tissues on ultrasound image. Moreover, speckle noise negatively impacts the subsequent stages in image processing pipeline, such as edge detec-tion, segmentation, feature extraction, and classification. Previous studies have formulated vari-ous speckle reduction methods in ultrasound images; however, these methods suffer from being unable to retain finer edge details and require more processing time. In this study, we propose a breast ultrasound de-speckling method based on rotational invariant block matching non-local means (RIBM-NLM) filtering. The effectiveness of our method has been demonstrated by com-paring our results with three established de-speckling techniques, the switching bilateral filter (SBF), the non-local means filter (NLMF), and the optimized non-local means filter (ONLMF) on 250 images from public dataset and 6 images from private dataset. Evaluation metrics, including Self-Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) were utilized to measure performance. With the proposed method, we were able to record average SSIM of 0.8915, PSNR of 65.97, MSE of 0.014, RMSE of 0.119, and computational speed of 82 seconds at noise variance of 20dB using the public dataset, all with -value of less than 0.001 compared against NLMF, ONLMF, and SBF. Similarly, the proposed method achieved av-erage SSIM of 0.83, PSNR of 66.26, MSE of 0.015, RMSE of 0.124, and computational speed of 83 seconds at noise variance of 20dB using the private dataset, all with -value of less than 0.001 compared against NLMF, ONLMF, and SBF.
超声技术是年轻女性乳腺癌诊断中不可或缺的成像方式，因为它能够有效地捕捉组织特性，降低阴性识别率，从而避免不必要的活检。尽管有这些优点，但超声图像会受到斑点噪声的影响，产生细微的伪结构，降低图像对比度，模糊超声图像上组织的实际边界。此外，斑点噪声对图像处理流程的后续阶段有负面影响，如图像边缘检测、分割、特征提取和分类。先前的研究已经提出了各种超声图像斑点减少方法；然而，这些方法存在无法保留更精细边缘细节且处理时间较长的问题。在本研究中，我们提出了一种基于旋转不变块匹配非局部均值（RIBM-NLM）滤波的乳腺超声去斑方法。通过将我们的结果与三种已有的去斑技术——切换双边滤波器（SBF）、非局部均值滤波器（NLMF）和优化非局部均值滤波器（ONLMF）——在来自公共数据集的250幅图像和来自私人数据集的6幅图像上进行比较，证明了我们方法的有效性。使用包括自相似性指数测量（SSIM）、峰值信噪比（PSNR）和均方误差（MSE）在内的评估指标来衡量性能。使用公共数据集时，在噪声方差为20dB的情况下，我们提出的方法能够记录平均SSIM为0.8915、PSNR为65.97、MSE为0.014、均方根误差（RMSE）为0.119，计算速度为82秒，与NLMF、ONLMF和SBF相比，所有p值均小于0.001。同样，使用私人数据集时，在噪声方差为20dB的情况下，所提出的方法实现了平均SSIM为0.83、PSNR为66.26、MSE为0.015、RMSE为0.124，计算速度为83秒，与NLMF、ONLMF和SBF相比，所有p值均小于0.001。
