Farag Amal, Elhabian Shireen, Graham James, Farag Aly, Elshazly Salwa, Falk Robert, Mahdi Hani, Abdelmunim Hossam, Al-Ghaafary Sahar
Department of Electrical & Computer Eng., University of Louisville, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3618-21. doi: 10.1109/IEMBS.2010.5627446.
A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer. We show the performance of the new nodule models on clinical datasets which illustrates significant improvements in both sensitivity and specificity.
本文提出了一种新方法，用于生成人类胸部低剂量CT（LDCT）扫描中出现的肺结节的数据驱动模型。使用主动外观模型方法分析四种常见类型的肺结节，以创建描述性肺结节模型。给定一个描述性数据库，该方法也适用于将结节自动分类为不同的病理类型。这种方法是肺癌早期诊断的一个重大进步。我们展示了新结节模型在临床数据集上的表现，结果表明在敏感性和特异性方面都有显著提高。
