Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection
Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection
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DOI：
10.1109/tmi.2009.2022630
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发表时间：
2010-03-01
影响因子：
10.6
通讯作者：
Hamamoto, Kazuhiko
中科院分区：
文献类型：
--
作者：
Takemura, Atsushi;Shimizu, Akinobu;Hamamoto, Kazuhiko
本文提出了一种新的估计对数压缩K分布参数的算法，并提出了一种超声图像中乳腺肿瘤的识别算法。我们总共计算了208个特征用于识别，包括基于对数压缩k分布参数的特征，该参数量化了肿瘤中回声模式的均匀性，但受到超声设备中压缩参数的影响。提出的算法以一种不受这种影响的方式估计对数压缩k分布的参数。为了量化肿瘤形状的不规则性，本文新发展了基于模式谱的特征。识别过程使用由多类AdaBoost学习算法（AdaBoost）训练的集成分类器。M2)，结合顺序特征选择过程。10倍交叉验证测试验证了性能，并将结果与基于马氏距离的分类器和多类支持向量机的结果进行了比较。实验共使用了200例癌、50例纤维腺瘤和50例囊肿。本文演示了AdaBoost训练的分类器的组合。M2和基于对数压缩k分布的估计参数的特征，以及模式谱的特征，对于肿瘤的识别是有用的。
This paper proposes a novel algorithm to estimate a log-compressed K distribution parameter and presents an algorithm to discriminate breast tumors in ultrasonic images. We computed a total of 208 features for discrimination, including those based on a parameter of a log-compressed K-distribution, which quantifies the homogeneity of the echo pattern in the tumor, but is influenced by compression parameters in the ultrasonic device. The proposed algorithm estimates the parameter of the log-compressed K-distribution in a manner free from this influence. To quantify irregularities in tumor shape, pattern-spectrum-based features were newly developed in this paper. The discrimination process uses an ensemble classifier trained by a multiclass AdaBoost learning algorithm (AdaBoost.M2), combined with a sequential feature-selection process. A 10-fold cross-validation test validated the performance, and the results were compared with those of a Mahalanobis distance-based classifier and a multiclass support vector machine. A total of 200 carcinomas, 50 fibroadenomas, and 50 cysts were used in the experiments. This paper demonstrates that the combination of a classifier trained by AdaBoost.M2 and features based on the estimated parameter of a log-compressed K-distribution, as well as those of the pattern spectrum, are useful for the discrimination of tumors.