基于改进局部三元模式的乳腺癌预测模型

目的 探讨基于改进局部三元模式（LTP）算法提取的乳腺新型纹理特征及其与常规特征融合预测乳腺癌的价值。方法 对钼靶图像进行乳腺分割，采用基于改进LTP算法提取双侧乳腺的新型纹理特征和常规特征；合并左右侧乳腺纹理特征；采用主成分分析法对提取的高维纹理特征降维；以K最近邻（KNN）和LADTree算法分别对纹理特征及融合纹理特征进行分类。结果 基于改进LTP算法提取的新型纹理特征预测乳腺癌的ROC曲线下面积（AUC）为0.732 4±0.042 8，敏感度为72.04%（67/93），特异度为74.51%（76/102），准确率为73.33%（143/195）；融合常规特征后AUC为0.865 5±0.014 8，敏感度为84.95%（79/93），特异度为88.23%（90/102），准确率为86.67%（169/195）。结论 基于LTP算法提取的新型纹理特征预测乳腺癌的精度较高，与常规特征融合后可进一步提高预测效能。

Breast cancer risk prediction model based on improved local ternary pattern algorithm

Objective To explore the value of new and fused conventional texture features extracted from mammograms using improved local ternary patterns (LTP) in predicting risk of breast cancer. Methods Mammograms were segmented. Based on improved LTP, the new and conventional texture features were extracted from segmented mammograms of bilateral breasts. Then the features of bilateral breasts were merged. The high dimensional characteristics were reduced with principal component analysis (PCA). Finally, the new texture features were classified with k-nearest neighbor (KNN), and the fusion features were clustered with logistic alternating decision tree (LADTree) algorithm. Results The area under ROC curve (AUC) of new texture features for predicting breast cancer was 0.732 4±0.042 8, and the sensitivity, specificity and prediction accuracy was 72.04%(67/93), 74.51%(76/102) and 73.33%(143/195), respectively. Furthermore, AUC of fusion features was 0.865 5±0.014 8, the sensitivity, specificity and prediction accuracy was 84.95%(79/93), 88.23%(90/102) and 86.67%(169/195), respectively. Conclusion The new texture features based on improved LTP have high prediction accuracy for breast cancer, and the prediction efficacy can be improved after fusion with conventional features.