Lung tumor segmentation based on the multi-scale template matching and region growing

Traditional region growing is a semi-automatic segmentation method, which needs manually labeled seeds and is easily leaked to neighbor tissues. In this paper, a new automatic segmentation method based on template matching and improved region growing is proposed. The proposed method consists of three main steps: First, bone is removed from CT images. Second, a multi-scale Gaussian template matching method is used to locate the lung tumors in the three-dimension PET images, in order to eliminate the liver and heart with similar voxel values but with different texture. Third, the seeds are automatically obtained in the process of template matching, in order to make the segmentation process fully automatic. The Euclidean distance measure is added to the criterion of region growth, in order to accelerate the convergence of segmentation and avoid the over-segmentation effectively. The proposed method was tested on 10 PET-CT images from 5 patients with lung tumors, and the average DSC (Dice Similarity Coefficient), TPR (True Positive Ratio) and FPR (False Positive Ratio) were 86.91±5.83%, 85.78±7.34% and 0.033±0.003%, respectively.