孤立性肺结节的检测是肺癌早期诊断的关键。针对传统点增强滤波器虽然对结节增强具有很好的敏感性，但是却产生很多假阳性区域的问题，提出一种通过计算3维增强密度指数和判别规则来识别肺结节的方法。首先采用自适应双边滤波器对CT图像序列进行降噪和平滑处理；然后计算对应的Hessian矩阵及其特征值得到预增强系数，并获得感兴趣体区域，通过对预增强系数的分析来构造3维增强密度指数；最后应用判别规则对感兴趣体进行识别。针对两个肺部CT图像数据集对该方法进行了测试，结果表明，在识别孤立性肺结节方面该方法是有效的。

The detection of solitary pulmonary nodules (SPN) is proven to be of critical importance in early stage lung cancer diagnosis. Aiming at reducing the false positive regions caused by the dot enhancement filter which is sensitive to the lung nodules, a new recognition method based on calculation of three dimensional (3D) enhancement density index and decision rule is proposed. An adaptive bilateral filter is applied to reduce the noisy and smooth CT image sequences. Then, the pre enhancement coefficients and volume of interest (VOI) are obtained by computing the Hessian matrix and corresponding eigenvalues. After analyzing the distribution of pre enhancement coefficients, 3D enhancement density index is constructed. Finally, a decision rule is adopted to identify nodule candidates. The proposed method is tested on two lung CT image sets. The experimental results illustrate the efficiency of the proposed algorithm.