Computer aided detection of lung nodules based on voxel analysis utilizing support vector machines

Yang Liu, Jinzhu Yang, Dazhe Zhao, Jiren Liu
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引用次数: 18

Abstract

The detection of pulmonary nodules is proven to be of critical importance in early-stage lung cancer diagnosis. Many computer aided detection (CAD) methods combined with morphological approach and pattern recognition technology to identify lung nodules have been proposed to assist the radiologists to improve sensitivity of diagnosis. We present a computer aided lung nodule detection scheme based on analysis of enhanced voxel in three dimensional (3D) CT image. The method is multi-step, including lung fields segmentation, initial nodule candidates enhancement, enhanced voxel feature extraction, voxels classification with support vector machines (SVMs) and nodule decision rule. Two lung nodule data sets are employed to evaluate the performance of the computerized scheme. The experimental results illustrate the efficiency of the proposed method. We intend to improve the voxel enhancement procedure to increase the performance of the scheme.
基于体素分析的支持向量机肺结节计算机辅助检测
肺结节的检测在早期肺癌诊断中具有至关重要的意义。许多计算机辅助检测(CAD)方法结合形态学方法和模式识别技术来识别肺结节,以帮助放射科医生提高诊断的敏感性。我们提出了一种基于三维CT图像增强体素分析的计算机辅助肺结节检测方案。该方法是多步骤的,包括肺场分割、初始结节候选增强、增强体素特征提取、支持向量机体素分类和结节决策规则。使用两个肺结节数据集来评估计算机化方案的性能。实验结果表明了该方法的有效性。我们打算改进体素增强程序以提高方案的性能。
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