基于shearlet变换和特征向量的二值分割算法

Ladan Sharafyan Cigaroudy, N. Aghazadeh
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引用次数: 4

摘要

本文给出了一种用于管状结构物体提取的迭代算法,特别是容器的提取。为此,我们对图像进行分割,得到找到目标物体像素的二值图像。在我们的分割方法中,我们使用高斯尺度空间技术计算图像的离散梯度进行预分割。同时,为了对图像进行降噪,我们采用了紧框架剪切波变换。该算法在TFA迭代部分的基础上有一个迭代部分[2],但我们使用图像Hessian矩阵的特征向量对这部分进行改进。给出了该方法的理论性质。实验结果表明,该算法能够有效地识别均匀血管。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A binary-segmentation algorithm based on shearlet transform and eigenvectors
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.
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