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Benign and malignant breast tumors: Diagnosis using fractal measures

机译:良性和恶性乳腺肿瘤:分形测量诊断

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The work presents two measures of complexity, fractal dimension and lacunarity, in order to raise the precision in breast cancer diagnosis. A set of 40 cases of mammograms from patients corresponding to both benign (24 images) and malignant tumors (16 images) were analyzed. To improve the diagnostic process we proposed a method that combines the two fractal characteristics. For the processing of mammograms it was used two software programs, one for computing average fractal dimensions from the image contour, proposed by the authors, and the other (FracLac) to compute the average lacunarity on the binary image. In this way, classification rate increased from 90% (when using fractal dimension) to 100%. Finally, we proposed a framework for assisted diagnosis from the mentioned set of mammographic images.
机译:这项工作提出了两种复杂程度的测量方法:分形维数和腔隙度,以提高乳腺癌诊断的准确性。分析了一组来自患者的40例乳房X线照片,这些图像分别对应于良性(24幅图像)和恶性肿瘤(16幅图像)。为了改善诊断过程,我们提出了一种结合两种分形特征的方法。对于乳腺X线照片的处理,使用了两个软件程序,一个软件程序由作者提出,用于从图像轮廓计算平均分形维数,另一个软件(FracLac)计算二进制图像上的平均盲度。这样,分类率从90%(使用分形维数时)增加到100%。最后,我们从上述乳腺X线摄影图像中提出了辅助诊断的框架。

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