Characterizing mammographic images by using generic texture features.
Characterizing mammographic images by using generic texture features.
复制标题
使用通用纹理特征来表征乳腺 X 光图像。
DOI：
10.1186/bcr3163
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发表时间：
2012-04-10
期刊：
影响因子：
--
通讯作者：
Wittenberg T
中科院分区：
文献类型：
--
作者：
Häberle L;Wagner F;Fasching PA;Jud SM;Heusinger K;Loehberg CR;Hein A;Bayer CM;Hack CC;Lux MP;Binder K;Elter M;Münzenmayer C;Schulz-Wendtland R;Meier-Meitinger M;Adamietz BR;Uder M;Beckmann MW;Wittenberg T
虽然乳房x线摄影密度是乳腺癌的一个确定的危险因素，但由于缺乏自动化和标准化的测量方法，其在临床实践中的应用受到限制。本研究的目的是评估乳房x线照片中各种自动纹理特征作为乳腺癌的危险因素，并通过病例对照研究设计将其与乳房x线照片密度百分比（PMD）进行比较。病例-对照研究包括864例病例和418例对照。四百七十种特征被认为是乳腺癌的可能危险因素。这些特征包括统计特征、基于矩的特征、光谱能量特征和基于形式的特征。使用逻辑回归分析进行了一个复杂的变量选择过程，以确定与病例对照状态相关的特征。此外，评估PMD并将其纳入回归模型。在探索的470个图像分析特征中，有46个保留在最终的逻辑回归模型中。验证资料的曲线下面积为0.79，每标准差变化的比值比为2.88 （95% CI, 2.28 ~ 3.65）。添加PMD并没有改善最终模型。利用纹理特征来预测乳腺癌的风险似乎是可行的。在这项研究中，PMD没有显示出任何额外的价值。关于评估的特征，大多数分析工具似乎反映了乳房x线摄影密度，尽管一些特征与PMD无关。这些特征是否有助于提高预测准确性还有待更大规模的病例对照研究的研究。
Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.
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影响因子：
3.8
作者：
Gao, Jinnan;Warren, Ruth;Forbes, John F.
通讯作者：
Forbes, John F.
影响因子：
3.8
作者：
Fasching, Peter Andreas;Loehberg, Christian R.;Strick, Reiner
通讯作者：
Strick, Reiner
DOI：
10.1109/tit.1962.1057692
发表时间：
1962-01-01
期刊：
IRE TRANSACTIONS ON INFORMATION THEORY
影响因子：
--
作者：
HU, M
通讯作者：
HU, M
DOI：
10.1093/jnci/87.9.670
发表时间：
1995-05-03
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
BOYD, NF;BYNG, JW;YAFFE, MJ
通讯作者：
YAFFE, MJ
影响因子：
4.3
作者：
Chen, YQ;Nixon, MS;Thomas, DW
通讯作者：
Thomas, DW