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首页> 外文期刊>Journal of Digital Imaging >Fractal Analysis of Contours of Breast Masses in Mammograms
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Fractal Analysis of Contours of Breast Masses in Mammograms

机译：乳房X光照片中乳房肿块轮廓的分形分析

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摘要

Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. Breast masses present shape and gray-scale characteristics that vary between benign masses and malignant tumors in mammograms. Limited studies have been conducted on the application of fractal analysis specifically for classifying breast masses based on shape. The fractal dimension of the contour of a mass may be computed either directly from the 2-dimensional (2D) contour or from a 1-dimensional (1D) signature derived from the contour. We present a study of four methods to compute the fractal dimension of the contours of breast masses, including the ruler method and the box counting method applied to 1D and 2D representations of the contours. The methods were applied to a data set of 111 contours of breast masses. Receiver operating characteristics (ROC) analysis was performed to assess and compare the performance of fractal dimension and four previously developed shape factors in the classification of breast masses as benign or malignant. Fractal dimension was observed to complement the other shape factors, in particular fractional concavity, in the representation of the complexity of the contours. The combination of fractal dimension with fractional concavity yielded the highest area (A z ) under the ROC curve of 0.93; the two measures, on their own, resulted in A z values of 0.89 and 0.88, respectively.
机译：分形分析已被证明在表征形状和灰度复杂性的图像处理中很有用。乳房肿块的形态和灰度特征在乳房X线照片中的良性肿块和恶性肿瘤之间有所不同。分形分析的应用已经进行了有限的研究，专门用于基于形状对乳腺肿块进行分类。质量轮廓的分形维数可以直接从二维（2D）轮廓或从轮廓中导出的一维（1D）签名计算。我们介绍了四种计算乳腺肿块轮廓的分形维数的方法，包括标尺方法和应用于等值线的1D和2D表示的盒计数法。将该方法应用于111个乳房肿块轮廓的数据集。进行受试者工作特征（ROC）分析，以评估和比较分形维数和四个先前开发的形状因子在乳腺肿块为良性或恶性分类中的性能。在轮廓的复杂性表示中，观察到分形维数以补充其他形状因子，尤其是分数凹度。分形维数与分数凹度的组合在ROC曲线下的最大面积（A z ）为0.93；两种方法各自得出的A z 值分别为0.89和0.88。

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