| 韩勇森,韩宝三,孙京文,宋成利,闫士举.MR图像纹理特征融合诊断前列腺癌[J].中国医学影像技术,2019,35(5):769~773 |
| MR图像纹理特征融合诊断前列腺癌 |
| Value of fusion of MRI texture features in diagnosis of prostate cancer |
| 投稿时间：2018-10-08 修订日期：2019-03-17 |
| DOI：10.13929/j.1003-3289.201810026 |
| 中文关键词: 前列腺肿瘤 自适应阈值 局部三元模式 磁共振成像 纹理特征 |
| 英文关键词:prostatic neoplasms adaptive threshold local ternary pattern magnetic resonance imaging texture feature |
| 基金项目: |
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| 中文摘要: |
| 目的 探讨三维空间自适应局部三元模式（LTP）纹理特征、常规纹理特征以及灰度统计特征融合用于诊断前列腺癌的价值。方法 从MRI中分割出前列腺外周带，提取自适应LTP融合纹理特征纹理特征、常规纹理特征和灰度统计特征，采用Adaboost算法分别获得每个特征族群以及3个族群特征融合的模型，计算对前列腺癌的诊断效能。结果 三维空间自适应LTP融合纹理特征诊断前列腺癌的AUC为0.79±0.04，敏感度为78.31%（65/83），特异度为80.81%（80/99），准确率为79.67%（145/182）；常规纹理特征诊断前列腺癌的AUC为0.71±0.04，敏感度为72.29%（60/83），特异度为81.82%（81/99），准确率为77.47%（141/182）；灰度统计特征诊断前列腺癌的AUC为0.80±0.04，敏感度为78.31%（65/83），特异度为82.83%（82/99），准确率80.77%（147/182）；融合特征诊断前列腺癌的AUC为0.87±0.04，敏感度为86.75%（72/83），特异度为88.89%（88/99），准确率为87.91%（160/182）。结论 通过融合局部三元模式特征、常规纹理特征、灰度统计特征，可有效提高诊断前列腺癌的效能。 |
| 英文摘要: |
| Objective To explore the value of three-dimensional optimization of threshold local ternary pattern (LTP) texture features, conventional texture features and grayscale statistical features fusion features for diagnosis of prostate cancer. Methods The peripheral zone of prostate was segmented from multi-sequence MR images. The optimization of the threshold LTP texture features, the conventional texture features and the grayscale statistical features was extracted. The fusion features were classified with Adaboost algorithm. The diagnostic efficacy was analyzed. Results AUC of three-dimensional optimization of the threshold LTP fusion texture feature for predicting prostate cancer was 0.79±0.04, and the sensitivity, specificity and accuracy was 78.31% (65/83), 80.81% (80/99) and 79.67% (145/182), respectively. The AUC of conventional texture features for predicting prostate cancer was 0.71±0.04, and the sensitivity, specificity and accuracy was 72.29% (60/83), 81.82% (81/99), 77.47% (141/182), respectively. The AUC of grayscale statistical features for predicting prostate cancer was 0.80±0.04, and the sensitivity, specificity and accuracy was 78.31% (65/83), 82.83% (82/99), 80.77% (147/182), respectively. The AUC of fusion features for predicting prostate cancer was 0.87±0.04, and the sensitivity, specificity and accuracy was 86.75% (72/83), 88.89% (88/99) and 87.91% (160/182), respectively. Conclusion The diagnostic efficacy of prostate cancer can be effectively improved by fusing local ternary patterns features, conventional texture features and grayscale statistical texture features. |
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