| 净 亮,邵党国,相 艳,马 磊,熊 馨,杨朝强,袁 野.基于支持向量机的自适应均值滤波超声图像降噪[J].电子测量与仪器学报,2020,34(3):1-8 |
| 基于支持向量机的自适应均值滤波超声图像降噪 |
| Adaptive mean filtering ultrasonic image denoising based on support vector machine |
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| DOI： |
| 中文关键词: 超声图像 自适应滤波 支持向量机 斑点噪声抑制 |
| 英文关键词:ultrasound image adaptive filtering support vector machine speckle noise suppression |
| 基金项目:国家自然科学基金(61462054, 61732005, 61672271, 61741112)、云南省自然科学基金(2017FB098)、国家博士后面上科学基金(2016M592894XB)、云南省科技厅(2015FB135)、云南省重大科技项目(2018ZF017)资助 |
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| 中文摘要: |
| 医学超声图像在成像过程中由于超声散射回波的相互干渉，导致所成图像中出现难以与器官、组织等人体结构区分的斑点噪声，给后期的临床诊断和图像后续处理带来了极大的不便。针对超声图像中的斑点噪声，提出了一种基于支持向量机（SVM）的自适应均值滤波超声图像降噪模型。该方法利用SVM的分类特性，将超声图像中的噪声信号和非噪声信号作出区分，再将SVM的分类结果和均值滤波相结合去对噪声图像进行去噪。这样可以保证医学含噪图像的组织区域和细节特征做到最大保留，噪声区域获得最大的平滑处理。在实验部分，通过对物理体膜和人体超声肝脏图像分别进行实验，结果表明,该方法可以有效抑制并降低超声图像中的斑点噪声，并保留了其边缘特征,使得去噪图像的信噪比显著增加，是一种有效的医学超声图像降噪方法。 |
| 英文摘要: |
| Due to the mutual interference of the ultrasonic scattering echoes in the imaging process, it may cause speckle noise in the formed medical ultrasonic images and it is difficult to distinguish from the human body structure, such as organ, tissue, etc, so that brining about complications to the later clinical diagnosis and image subsequent processing. In order to process the speckle noise in ultrasound images, a noise reduction model is proposed for adaptive average filtering ultrasound image based on support vector machine (SVM). The method uses the classification characteristics of SVM to distinguish the noise signal and the non noise signal in the ultrasound image, then combines the SVM classification result and the average filtering to denoise for the noise image. This operate can ensure the tissue area and detail characteristic of the medically noisy image are maximumly retain while the noise area is maximumly smooth. In the experimental part, the method used on the physical body membrane and human ultrasound liver image respectively. The results show that the proposed method can effectively suppress and reduce the speckle noise in the ultrasound image, and retain its edge features, and the signal noise ratio of denoised image is increased. It can prove that the proposed method is useful for medical ultrasound image denoising. |
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