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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Using 3D Texture and Margin Sharpness Features on Classification of Small Pulmonary Nodules
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Using 3D Texture and Margin Sharpness Features on Classification of Small Pulmonary Nodules

机译：在小肺结节分类中使用3D纹理和边缘清晰度功能

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摘要

The lung cancer is the reason of a lot of deaths on population around the world. An early diagnosis brings a most curable and simpler treatment options. Due to complexity diagnosis of small pulmonary nodules, Computer-Aided Diagnosis (CAD) tools provides an assistance to radiologist aiming the improvement in the diagnosis. Extracting relevant image features is of great importance for these tools. In this work we extracted 3D Texture Features (TF) and 3D Margin Sharpness Features (MSF) from the Lung Image Database Consortium (LIDC) in order to create a classification model to classify small pulmonary nodules with diameters between 3-10mm. We used three machine learning algorithm: k-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP) and Random Forest (RF). These algorithms were trained by different set of features from the TF and MSF. The classification model with MLP algorithm using the selected features from the integration of TF and MSF achieved the best AUC of 0.820.
机译：肺癌是世界各地许多人死亡的原因。早期诊断带来了最可治愈和更简单的治疗选择。由于小肺结节的诊断很复杂，计算机辅助诊断（CAD）工具为放射科医生提供了帮助，旨在改善诊断水平。对于这些工具而言，提取相关的图像特征非常重要。在这项工作中，我们从肺图像数据库协会（LIDC）中提取了3D纹理特征（TF）和3D边缘清晰度特征（MSF），以便创建分类模型以对直径在3-10mm之间的小肺结节进行分类。我们使用了三种机器学习算法：k最近邻（k-NN），多层感知器（MLP）和随机森林（RF）。这些算法通过TF和MSF的不同功能集进行了训练。使用MLP算法的分类模型使用了从TF和MSF的集成中选择的功能，获得了0.820的最佳AUC。

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