一种基于活动轮廓模型的PET-CT肺肿瘤分割方法

A PET-CT Lung Tumor Segmentation Method Based on Active Contour Model

针对PET-CT肺肿瘤分割中存在的没有充分将医生临床经验融入到算法设计的问题,该文利用PET高斯分布先验,结合区域可伸缩拟合(RSF)模型和最大似然比分类(MLC)准则,提出一种基于变分水平集的混合活动轮廓模型RSF;L。进一步,借鉴人工勾画肺肿瘤过程中融合图像的重要价值,提出了基于RSF;L的PET-CT肺肿瘤融合图像分割方法。实验表明,所提出方法较好地实现了有代表性的非小细胞肺肿瘤(Non-Small Cell Lung Cancer,NSCLC)的精确分割,主客观结果优于对比方法,可为临床提供有效的计算机辅助分割结果。

To solve the problem that the doctors’clinical experience is not fully integrated into the algorithm design in PET-CT lung tumor segmentation,a hybrid active contour model named RSF_ML based on variational level set is proposed by combining with the PET Gaussian distribution prior,Region Scalable Fitting(RSF)model and Maximum Likelihood ratio Classification(MLC)criterion.Furthermore,referring to the important value of fusion image in the process of lung tumor manual delineation,a segmentation method for PET-CT lung tumor fusion image based on RSF_ML is proposed.Experiments show that the proposed method can achieve accurate segmentation of representative Non-Small Cell Lung Cancer(NSCLC),and the subjective and objective results are better than the comparison method,which can provide effective computeraided segmentation results for clinic.