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首页> 外文会议>Conference on computer-aided diagnosis >A New Breast Cancer Risk Analysis Approach Using Features Extracted from Multiple Sub-regions on Bilateral Mammograms
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A New Breast Cancer Risk Analysis Approach Using Features Extracted from Multiple Sub-regions on Bilateral Mammograms

机译：一种新的乳腺癌风险分析方法，使用了从双边乳房X线照片上多个子区域提取的特征

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A novel breast cancer risk analysis approach is proposed for enhancing performance of computerized breast cancer risk analysis using bilateral mammograms. Based on the intensity of breast area, five different sub-regions were acquired from one mammogram, and bilateral features were extracted from every sub-region. Our dataset includes 180 bilateral mammograms from 180 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including sub-region segmentation, bilateral feature extraction, feature selection, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under the curve (AUC) is 0.763 ± 0.021 when applying the multiple sub-region features to our testing dataset. The positive predictive value and the negative predictive value were 0.60 and 0.73, respectively. The study demonstrates that (1) features extracted from multiple sub-regions can improve the performance of our scheme compared to using features from whole breast area only; (2) a classifier using asymmetry bilateral features can effectively predict breast cancer risk; (3) incorporating texture and morphological features with density features can boost the classification accuracy.
机译：提出了一种新的乳腺癌风险分析方法，用于使用双侧乳房X线照片提高计算机化乳腺癌风险分析的性能。基于乳房区域的强度，从一个乳房点获取五个不同的子区域，从每个亚区域提取双侧特征。我们的数据集包括来自180名来自180名妇女的双边乳房X线照片，他们经历了常规筛查考试，所有这些都被解释为消极筛查过程中的负面且未回忆起原始筛查程序期间。使用四个图像处理模块的计算机化乳腺癌风险分析方案，包括子区域分割，双边特征提取，特征选择和分类，以检测和计算在乳房X光线照片上成像的左右乳房之间的图像特征不对称。在将多个子区域功能应用于测试数据集时，曲线下的最高计算区域为0.763±0.021。阳性预测值和负预测值分别为0.60和0.73。该研究表明（1）从多个子区域提取的特征可以改善与使用全乳房区域的功能相比的方案的性能; （2）使用不对称双侧特征的分类剂可以有效地预测乳腺癌风险; （3）含有密度特征的纹理和形态特征可以提高分类准确性。

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