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首页> 外文会议>Image Perception, Observer Performance, and Technology Assessment; Progress in Biomedical Optics and Imaging; vol.7 no.32 >Mass Detection in Mammographic ROIs Using Watson Filters
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Mass Detection in Mammographic ROIs Using Watson Filters

机译：使用Watson过滤器在乳腺X线摄影机ROI中进行质量检测

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摘要

Human vision models have been shown to capture the response of the visual system; their incorporation into the classification stage of a Computer Aided Detection system could improve performance. This study seeks to improve the performance of an automated breast mass detection system by using the Watson filter model versus a Laguerre Gauss Channelized Hotelling Observer (LG-CHO). The LG-CHO and the Watson filter model were trained and tested on a 512 x 512 ROI database acquired from the Digital Database of Screening Mammography consisting of 800 total ROIs; 200 of which were malignant, 200 were benign and 400 were normal. Half of the ROIs were used to train the weights for ten LG-CHO templates that were later used during the testing stage. For the Watson filter model, the training cases were used to optimize the frequency filter parameter empirically to yield the best ROC Az performance. This set of filter parameters was then tested on the remaining cases. The training Az for the LG-CHO and the Watson filter was 0.896 +/- 0.016 and 0.924 +/- 0.014 respectively. The testing Az for the LG-CHO and Watson filter was 0.849 +/- 0.019 and 0.888 +/- 0.017. With a p-value of 0.029, the difference in testing performance was statistically significant, thus implying that the Watson filter model holds promise for better detection of masses.
机译：已经显示出人类视觉模型可以捕捉视觉系统的响应；将其合并到计算机辅助检测系统的分类阶段可以提高性能。这项研究旨在通过使用Watson过滤器模型与Laguerre Gauss通道化旅馆业观察者（LG-CHO）来提高自动乳房质量检测系统的性能。 LG-CHO和Watson滤波器模型在512 x 512 ROI数据库中进行了训练和测试，该数据库是从X线筛查钼靶数字数据库中获得的，该数据库包含800个ROI。其中200例为恶性，200例为良性，400例为正常。一半的投资回报率用于训练十个LG-CHO模板的权重，这些模板随后在测试阶段使用。对于Watson滤波器模型，训练案例用于根据经验优化频率滤波器参数，以产生最佳的ROC Az性能。然后在其余情况下测试这组过滤器参数。 LG-CHO和Watson滤波器的训练Az分别为0.896 +/- 0.016和0.924 +/- 0.014。 LG-CHO和Watson滤波器的测试Az为0.849 +/- 0.019和0.888 +/- 0.017。 p值为0.029时，测试性能的差异在统计上是显着的，因此表明Watson滤波器模型有望更好地检测质量。

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