A robust algorithm for the fractal dimension of images and its applications to the classification of natural images and ultrasonic liver images
A robust algorithm for the fractal dimension of images and its applications to the classification of natural images and ultrasonic liver images
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DOI：
10.1016/j.sigpro.2009.12.010
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发表时间：
2010-06
期刊：
影响因子：
--
通讯作者：
Wen-Li Lee;Kai-Sheng Hsieh
中科院分区：
文献类型：
--
作者：
Wen-Li Lee;Kai-Sheng Hsieh
分形维数的计算是分形几何中的一个关键问题。流行的方法是基于盒子计数。然而，该方案很容易受到噪声的干扰，并产生许多不可忽略的平台，导致低估。本文提出了一种更加稳健和有效的计算分形维数的方法。为了验证其性能，基于分形维数和M-带小波变换的特征向量应用于自然纹理图像和超声肝脏图像的分类基于四种不同的分类器。实验结果表明，该计算方法是可靠的.
The calculation of the fractal dimension is crucial in fractal geometry. The popular approach is based on box-counting. However, this scheme is easily disturbed by noise and produces many non-negligible plateaus that cause an underestimation. This paper proposes a more robust and efficient method for computing the fractal dimension. To validate its performance, a feature vector based on fractal dimension and M-band wavelet transform was applied to the classification of natural textured images and ultrasonic liver images based on four different classifiers. The experimental results revealed the proposed computation method is trustworthy.