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Application of the compound probability density function for characterization of breast masses in ultrasound B scans

机译:复合概率密度函数在B超检查中表征乳腺肿块的应用

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摘要

The compound probability density function (pdf) is investigated for the ability of its parameters to classify masses in ultrasonic B scan breast images. Results of 198 images (29 malignant and 70 benign cases and two images per case) are reported and compared to the classification performance reported by us earlier in this journal. A new parameter, the speckle factor, calculated from the parameters of the compound pdf was explored to separate benign and malignant masses. The receiver operating characteristic curve for the parameter resulted in an A, value of 0.852. This parameter was combined with one of the parameters from our previous work, namely the ratio of the K distribution parameter at the site and away from the site. This combined parameter resulted in an A, value of 0.955. In conclusion, the parameters of the K distribution and the compound pdf may be useful in the classification of breast masses. These parameters can be calculated in an automated fashion. It should be possible to combine the results of the ultrasonic image analysis with those of traditional mammography, thereby increasing the accuracy of breast cancer diagnosis.
机译:研究了复合概率密度函数(pdf)的参数对超声波B扫描乳腺图像中的肿物进行分类的能力。报告了198幅图像(29例恶性和70例良性病例,每例2幅图像)的结果,并将其与我们在本杂志之前报道的分类性能进行了比较。探索了一个新的参数,散斑因子,由化合物pdf的参数计算得出,以区分良性和恶性肿块。该参数的接收器工作特性曲线的A值为0.852。该参数与我们先前工作的参数之一结合在一起,即现场和远离现场的K分布参数之比。该组合参数得出的A值为0.955。总之,K分布参数和化合物pdf可能对乳腺肿块的分类有用。这些参数可以自动方式计算。应该可以将超声图像分析的结果与传统的乳房X线照相术的结果相结合,从而提高乳腺癌诊断的准确性。

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