论文标题
基于小波领导者的形式主义，以计算X射线图像中的肺结节分类的多重特征
Wavelet leader based formalism to compute multifractal features for classifying lung nodules in X-ray images
论文作者
论文摘要
本文介绍并验证了一种新型的肺结核分类算法，该算法使用X射线图像中发现的多重分子特征。提出的方法包括一个预处理步骤，其中应用了两种增强技术：直方图均衡和小波分解和形态操作的组合。作为一种新颖性，使用基于小波领导者的形式主义的多型特征与支持向量机分类器一起使用。还包括其他经典纹理功能。当使用多重分子特征与经典纹理特征结合使用时，获得了最佳结果，最大ROC AUC为75 \％。结果显示使用数据增强技术和参数优化时的改进。在类似的实验设置中比较时，所提出的方法在计算成本和准确性方面被证明比模量最大小波形式更有效，更准确。
This paper presents and validates a novel lung nodule classification algorithm that uses multifractal features found in X-ray images. The proposed method includes a pre-processing step where two enhancement techniques are applied: histogram equalization and a combination of wavelet decomposition and morphological operations. As a novelty, multifractal features using wavelet leader based formalism are used with Support Vector Machine classifier; other classical texture features were also included. Best results were obtained when using multifractal features in combination with classical texture features, with a maximum ROC AUC of 75\%. The results show improvements when using data augmentation technique, and parameter optimization. The proposed method proved to be more efficient and accurate than Modulus Maxima Wavelet Formalism in both computational cost and accuracy when compared in a similar experimental set up.
