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A binary-segmentation algorithm based on shearlet transform and eigenvectors

机译:基于剪波变换和特征向量的二进制分割算法

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In this paper, we illustrate an iterative algorithm for extraction of object with tubular structure specially vessel extraction. For this aim, we segment image to reach binary image in which the pixels of purpose object is found. In our segmentation method, we use Gaussian scale-space technique to compute discrete gradient of image for pre-segmenting. Also, in order to denoise, we use tight frame of shearlet transform. This algorithm has an iterative part based on iterative part of TFA [2], but we use eigenvectors of Hessian matrix of image for improving this part. Theoretical properties of this method are presented. The experimental results show that in our algorithm distinguishing homogeneous vessels is done efficiently.
机译:在本文中,我们说明了一种使用管状结构特别是容器提取来提取对象的迭代算法。为此,我们将图像分割为二值图像,在该图像中找到目标对象的像素。在我们的分割方法中,我们使用高斯尺度空间技术来计算图像的离散梯度以进行预分割。另外,为了进行去噪,我们使用了紧波变换的紧帧。该算法具有基于TFA [2]的迭代部分的迭代部分,但是我们使用图像的Hessian矩阵的特征向量对此部分进行了改进。介绍了该方法的理论性质。实验结果表明,在我们的算法中,有效区分均质血管是有效的。

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