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Speckle Reduction of Ultrasound Image via Morphological Based Edge Preserving Weighted Mean Filter

机译:通过基于形态的边缘保持加权平均滤波器的超声图像散斑减少

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Medical ultrasound images suffer from inherently generated speckle noise that makes difficult the radiologists to diagnose the diseases. Proper speckle reduction technique is required to improve quality of image which may help the doctor to diagnose the diseases correctly. Speckle removal is, specially, a filtering technique that reduces the amount of speckle noise. But as an effect of filtering, the edges of objects may become blur and fine details within the image may be lost. Many methods have already been proposed to achieve these two requirements. But they do not satisfy both up to the desirable level. In this paper, we propose a novel morphological operation based speckle filter which reduces the effect of speckle noise to the greater extent in one side and keeps detail information in other side. The use of morphological operators helps in extracting the structures present within the images followed by an edge preserving weighted mean filtering method that may help in despeckling of ultrasound images. The proposed technique is applied on the ultrasound simulated data as well as on the real data obtained from the ultrasound machine. The quantitative results and the output images confirm the superiority of the method when compared with some of the other contemporary speckle reduction methods.
机译:医学超声图像遭受固有的产生散斑噪声,使放射科医师难以诊断疾病。需要适当的散斑减少技术来提高图像的质量,这可以帮助医生正确诊断疾病。特殊地,散斑拆卸是一种减少散斑噪声量的过滤技术。但作为过滤的效果,物体的边缘可以变为模糊,图像内的细节可能丢失。已经提出了许多方法来实现这两个要求。但它们不满足理想的水平。在本文中,我们提出了一种基于新颖的形态学操作的散斑滤波器,其在一侧的程度上降低了散斑噪声的影响,并在另一边保持细节信息。使用形态运算符有助于提取图像内的结构,然后是边缘保留的加权平均滤波方法,其可以帮助考虑超声图像。所提出的技术应用于超声模拟数据以及从超声波机器获得的真实数据。定量结果和输出图像与其他当代斑点减少方法相比,确认了该方法的优越性。

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