Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound
Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound
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DOI：
10.1177/0954411912472422
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发表时间：
2013-01-01
期刊：
影响因子：
1.8
通讯作者：
Suri, Jasjit S.
中科院分区：
文献类型：
--
作者：
Acharya, U. Rajendra;Sree, S. Vinitha;Suri, Jasjit S.
超声检查在鉴别甲状腺良恶性结节方面有很大的潜力。然而，视觉解译受到观察者间可变性的限制，此外，散斑分布在分类过程中提出了挑战。因此，本文提出了一个三维超声造影数据集的肿瘤分类自动化系统。该系统首先使用基于复小波变换的滤波器处理对比度增强超声图像，以减轻斑点噪声的影响。然后提取高阶光谱特征并将其作为训练和测试模糊分类器的输入。在离线训练系统中，从一组称为训练图像的图像中提取高阶光谱特征。这些高阶光谱特征与临床分配的地面真值一起用于训练分类器并获得分类器或训练参数的估计。ground truth表示图像的类别标签（即图像属于良性结节还是恶性结节）。在在线测试阶段，将估计的分类器参数应用于从测试图像中提取的高阶光谱特征，以预测其类别标签。将预测的类标签与其相应的原始真值进行比较，以评估分类器的性能。在不使用复小波变换滤波器的情况下，模糊分类器的准确率为91.6%，而使用复小波变换滤波器的准确率显著提高到99.1%。
Ultrasonography has great potential in differentiating malignant thyroid nodules from the benign ones. However, visual interpretation is limited by interobserver variability, and further, the speckle distribution poses a challenge during the classification process. This article thus presents an automated system for tumor classification in three-dimensional contrast-enhanced ultrasonography data sets. The system first processes the contrast-enhanced ultrasonography images using complex wavelet transform-based filter to mitigate the effect of speckle noise. The higher order spectra features are then extracted and used as input for training and testing a fuzzy classifier. In the off-line training system, higher order spectra features are extracted from a set of images known as the training images. These higher order spectra features along with the clinically assigned ground truth are used to train the classifier and obtain an estimate of the classifier or training parameters. The ground truth tells the class label of the image (i.e. whether the image belongs to a benign or malignant nodule). During the online testing phase, the estimated classifier parameters are applied on the higher order spectra features that are extracted from the testing images to predict their class labels. The predicted class labels are compared with their corresponding original ground truth to evaluate the performance of the classifier. Without utilizing the complex wavelet transform filter, the fuzzy classifier demonstrated an accuracy of 91.6%, while utilizing the complex wavelet transform filter, the accuracy significantly boosted to 99.1%.