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首页> 外文会议>Mediterranean Conference on Medical and Biological Engineering and Computing >Tensor Radial Lengths for Mammographic Image Enhancement
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Tensor Radial Lengths for Mammographic Image Enhancement

机译：乳房X线图图像增强的张力径向长度

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Proper enhancement of mammographic images in order to reveal diagnostic-ally critical information is a very important task in everyday clinical practice. As the volume of screening mammograms increases so does the importance of algorithms that can reveal tumors or other kind of lesions. In this paper a mammogram enhancement method, inspired from the concepts of tensor image representation and tensor scale, is presented. In particular a number of tensor radial lengths is defined at each image location and their mean value is then subtracted from the original image. The proposed method was tested on a dataset of 192 images containing mass lesions taken from the Digital Database for Screening Mammography providing quite promising results. Furthermore the enhancement performance of the proposed method was compared with the enhancement performance of Contrast Limited Adaptive Histogram Equalization, Histogram Equalization and Unsharp Masking methods and clearly outperformed them.
机译：正确增强乳房X XMPoxt图像，以揭示诊断 - 盟友关键信息是日常临床实践中非常重要的任务。随着筛选乳房X线照片的体积增加，可以揭示肿瘤或其他病变的算法的重要性。在本文中，提出了一种乳房X光检查方法，其激发了来自张量图像表示和张量刻度的概念。特别地，在每个图像位置处限定许多张量径向长度，然后从原始图像中减去它们的平均值。在含有来自数字数据库的含有质量病变的192个图像的数据集上进行测试，用于筛选乳房X线摄影，提供了非常有前途的结果。此外，将该方法的增强性能与对比度有限的自适应直方图均衡，直方图均衡和unsharp掩蔽方法的增强性能进行了比较，并且显然优于它们。

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