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Segmentation of medical ultrasound images based on level set method with edge representing mask

机译:基于边缘表示遮罩的水平集方法分割医学超声图像

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This paper presents a new extended Chan-Vese level set method for ultrasound image segmentation. The proposed method introduces wavelet multiresolution analysis to create an edge representing mask to spot edge information of the image. While the evolution of the level set function, average edge energy of zero level set curve is calculated from the edge representing mask to control the evolving speed. The proposed method has some advantages compared with traditional Chan-Vese method as follows. First, the proposed method is robust to the inherent speckle noise in ultrasound images. Second, the method is sensitive to intensity inhomogeneity of objects in images. Third, the method can solve the segmentation of ultrasound images with weak or missing boundaries. We apply the proposed method to synthetic and medical ultrasound images, and the results suggest that this method is superior to the traditional Chan-Vese level set method.
机译:本文提出了一种新的扩展的Chan-Vese水平集方法,用于超声图像分割。所提出的方法引入了小波多分辨率分析,以创建代表掩模的边缘以斑点图像的边缘信息。在水平设置函数的演化过程中,从代表蒙版的边缘计算零水平设置曲线的平均边缘能量,以控制演化速度。与传统的Chan-Vese方法相比,该方法具有以下优点。首先,所提出的方法对超声图像中固有的斑点噪声具有鲁棒性。其次,该方法对图像中物体的强度不均匀性敏感。第三,该方法可以解决边界弱或缺失的超声图像的分割。我们将提出的方法应用于合成和医学超声图像,结果表明该方法优于传统的Chan-Vese水平集方法。

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