A 3D Segmentation and Visualization Scheme for Solid and Non-solid Lung Lesions Based on Gaussian Filtering Regularized Level Set

The segmentation of Lung lesions is a challenging task because of the complexity of lung lesions surroundings. Lung lesions can be categorized into two types: solid and non-solid. Lots of works have been developed previously to segment one of two types, but only a few are proposed to handle two types at the same time and these methods may be over-segmented or sub-segmented. Therefore, in this study, an effective framework is designed to segment two types of lung lesions in 3-dimension (3D). In the proposed framework, we use a Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method to produce a 3D rough segmentation which is used as the initial contour of Geodesic Active Contour (GAC) method. SBGFRLS method can deal with the non-solid lung lesions very well because it can use the global information to segment inhomogeneous entities. GAC method can accurately locate the edge using the local information. Finally, we reconstruct and visualize the 3D segmentation results of lung lesions using visualization toolkit (VTK). All of our work is based on the Image Segmentation and Registration Toolkit (ITK) platform. We evaluate our method on the lung lesions CT data sets from 300 patients (280 for solid and 20 for non-solid). Experimental results show that our method can achieve better segmentation and more accurate calculation of 3D volume measurement compared to other two methods, especially in the non-solid type lesions.