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首页> 外文期刊>International Journal of Image, Graphics and Signal Processing >New Algorithm for Fractal Dimension Estimation based on Texture Measurements: Application on Breast Tissue Characterization
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New Algorithm for Fractal Dimension Estimation based on Texture Measurements: Application on Breast Tissue Characterization

机译:基于纹理测量的分形维数估计新算法:在乳腺组织表征中的应用

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摘要

Fractal analysis is currently in full swing in particular in the medical field because of the fractal nature of natural phenomena (vascular system, nervous system, bones, breast tissue ...). For this, many algorithms for estimating the fractal dimension have emerged. Most of them are based on the principle of box counting. In this work we propose a new method for calculating fractal attributes based on contrast homogeneity and energy that have been extracted from gray level co-occurrence matrix. As application we are investigated in the characterization and classification of mammographic images with SuportVectorMachine classifier. We considered in particular images with tumor masses and architectural disorder to compare with normal ones. We calculate, for comparison the fractal dimension obtained by a reference method (triangular prism) and perform a classification similar to the previous. Results obtained with new algorithm are better than reference method (classification rate is 0.91 vs 0.65). Hence new fractal attributes are relevant.
机译:由于自然现象(血管系统,神经系统,骨骼,乳腺组织……)的分形特性,分形分析目前正在特别是医学领域中全面展开。为此,出现了许多估计分形维数的算法。它们中的大多数都是基于盒子计数的原理。在这项工作中,我们提出了一种新的计算方法,该方法基于对比度同质性和从灰度共生矩阵中提取的能量来计算分形属性。作为应用程序,我们使用SuportVectorMachine分类器研究了乳腺X线照片的特征和分类。我们特别考虑了具有肿瘤块和建筑障碍的图像,以与正常图像进行比较。为了进行比较,我们计算通过参考方法(三角棱镜)获得的分形维数,并执行与先前类似的分类。使用新算法获得的结果优于参考方法(分类率为0.91对0.65)。因此,新的分形属性是相关的。

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