P3E-7 A New Feature For Detection Of Prostate Cancer Based On RF Ultrasound Echo Signals
P3E-7 A New Feature For Detection Of Prostate Cancer Based On RF Ultrasound Echo Signals
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P3E-7 基于射频超声回波信号检测前列腺癌的新功能
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
P. Mousavi
中科院分区：
文献类型：
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作者：
Mehdi Moradi;P. Abolmaesumi;P. Isotalo;D. Siemens;E.R. Sauerbrei;P. Mousavi
在本文中，我们描述了一种新的方法来检测前列腺癌的组织定征。我们提出，如果前列腺组织中的特定位置经历与超声的连续相互作用，则来自该位置的RF回波信号的时间序列将携带“组织表征”信息。这种现象是由于正常组织和癌组织的不同微观结构。我们使用Higuchi的方法来计算RF回波时间序列的分形维数作为复杂性的度量。前列腺组织的感兴趣区域上的平均分形维数被用作唯一的组织表征特征，并与贝叶斯分类器一起沿着应用。结果验证的基础上详细的恶性肿瘤的组织病理学地图。ROC曲线下面积为0.894，准确率高达86%，表明基于RF时间序列分形分析的组织定征方法的有效性
In this paper we describe a new approach to tissue characterization for detection of prostate cancer. We propose that if a specific location in the prostate tissue undergoes continuous interactions with ultrasound, the time series of RF echo signals from that location would carry "tissue characterizing" information. This phenomenon is due to different microstructures of normal and cancerous tissues. We use Higuchi's methodology to compute the fractal dimension of RF echo time series as a measure of the complexity. Averaged fractal dimension over a region of interest of the prostate tissue is utilized as the sole tissue characterizing feature and applied along with a Bayesian classifier. The results are validated based on detailed histopathologic maps of malignancy. The area under ROC curve is 0.894 and accuracies of up to 86% are acquired, indicating the effectiveness of our tissue characterization approach based on the fractal analysis of RF time series