Shankar P M, Piccoli C W, Reid J M, Forsberg F, Goldberg B B
Department of Electrical and Computer Engineering, Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA.
Phys Med Biol. 2005 May 21;50(10):2241-8. doi: 10.1088/0031-9155/50/10/004. Epub 2005 Apr 27.
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(z) 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(z) 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.
研究了复合概率密度函数(pdf)的参数对超声B超乳腺图像中肿块进行分类的能力。报告了198幅图像(29例恶性病例和70例良性病例,每例两幅图像)的结果,并与我们此前在本期刊上报告的分类性能进行了比较。探索了一个根据复合pdf参数计算得出的新参数——散斑因子,以区分良性和恶性肿块。该参数的接收器操作特征曲线得出的A(z)值为0.852。这个参数与我们之前工作中的一个参数相结合,即肿块部位和远离肿块部位的K分布参数之比。这个组合参数得出的A(z)值为0.955。总之,K分布和复合pdf的参数可能有助于乳腺肿块的分类。这些参数可以自动计算得出。将超声图像分析结果与传统乳腺X线摄影结果相结合应该是可行的,从而提高乳腺癌诊断的准确性。