Petrick N, Chan H P, Wei D, Sahiner B, Helvie M A, Adler D D
University of Michigan, Department of Radiology, Ann Arbor 48109-0030, USA.
Med Phys. 1996 Oct;23(10):1685-96. doi: 10.1118/1.597756.
This paper presents segmentation and classification results of an automated algorithm for the detection of breast masses on digitized mammograms. Potential mass regions were first identified using density-weighted contrast enhancement (DWCE) segmentation applied to single-view mammograms. Once the potential mass regions had been identified, multiresolution texture features extracted from wavelet coefficients were calculated, and linear discriminant analysis (LDA) was used to classify the regions as breast masses or normal tissue. In this article the overall detection results for two independent sets of 84 mammograms used alternately for training and test were evaluated by free-response receiver operating characteristics (FROC) analysis. The test results indicate that this new algorithm produced approximately 4.4 false positive per image at a true positive detection rate of 90% and 2.3 false positives per image at a true positive rate of 80%.
本文介绍了一种用于在数字化乳腺钼靶图像上检测乳腺肿块的自动算法的分割和分类结果。首先使用应用于单视图乳腺钼靶图像的密度加权对比度增强（DWCE）分割来识别潜在的肿块区域。一旦识别出潜在的肿块区域，就计算从小波系数中提取的多分辨率纹理特征，并使用线性判别分析（LDA）将这些区域分类为乳腺肿块或正常组织。在本文中，通过自由响应接收器操作特性（FROC）分析评估了交替用于训练和测试的两组独立的84幅乳腺钼靶图像的总体检测结果。测试结果表明，这种新算法在真阳性检测率为90%时，每张图像产生约4.4个假阳性，在真阳性率为80%时，每张图像产生2.3个假阳性。
