由于部分实性肺结节( pGGO) 中的实性成分存在亮度不均匀和边界模糊等问题， 传统的活动轮廓模型很难取得精确的分割结果． 为此，文中提出了一种改进的小波能量引 导下的活动轮廓模型来完成 pGGO 中实性成分的分割． 首先，通过小波变换将图像的灰度 信息转变成小波系数，对低通小波系数进行模糊化，以抑制图像局部区域过增强和欠增 强，同时结合高通小波系数计算图像的小波能量并构建活动轮廓模型的区域项，以加强肺 结节中实性成分与周围磨玻璃影的区分;然后，利用高斯混合模型计算肺结节图像的后验 概率，将后验概率差作为活动轮廓模型的边界检测函数，使得在实性成分的边界处边界检 测函数趋于0，轮廓曲线停止演变． 实验结果显示，文中提出的模型得到的真阳性率为 0． 95、假阳性率为0． 23 和相似度为0． 80，有助于 pGGO 中实性成分的确定． 

Due to the intensity inhomogeneity and fuzzy boundary of the solid components in partial ground glass opacity ( pGGO) pulmonary nodules，it is difficult to obtain accurate segmentation results through the traditional active contour model． Thus an improved wavelet-energy guided active contour model was proposed for solid component segmentation in pGGO． First，the grayscale information of the image was converted into wavelet coefficients by wavelet transform． The low-pass coefficients were fuzzified to suppress over-enhancement and under-enhancement regions． By integrating high-pass wavelet coefficients and the fuzzified low-pass coefficients，wavelet energy was calculated to build the region term of the active contour model to enhance the dissimilarity between solid part and surrounding ground glass part． Then， the Gaussian mixture model was used to calculate the posterior probability of the pulmonary nodule image． When the posterior probability difference is selected as the boundary detection function， the boundary detection function tended to zero at the boundary of the solid component in pGGO，and the proposed active contour curve stopped evolution． The experimental results show that the model proposed in this paper has a true positive rate of 0. 95，a false positive rate of 0. 23 and a similarity of 0. 80，which contributes to the determination of the solid components in pGGO．