Segmentation of Lung Tumor in Cone Beam CT Images Based on Level-Sets
Bijju Kranthi Veduruparthi, Jayanta Mukhopadhyay, Partha Pratim Das, Mandira Saha, Sriram Prasath, Soumendranath Ray, Raj Kumar Shrimali, Sanjoy Chatterjee · 2018
Automatic segmentation of tumor in low dose scans like the Cone Beam Computed Tomography (CBCT) is quite challenging. We use a semi-automatic approach to segment tumor from non tumor using the classical level-set formulation. A pipeline of techniques, mainly involving gradient-based level-sets (GB) and Local Rank Transform (LRT) is used to achieve the tumor segmentation. Since CBCT images are prone to noise, the edge strength at the tumor and non-tumor boundary is very low. To improve the edge strength in the CBCT image, we propose to use the edges obtained from the LRT-attractor of the image. The gradient-based level-sets with LRT-attractor (GBLA) is a non-linear technique that helps in strengthening the latent tumor and non-tumor boundary. We compare the GBLA level-sets with the GB level-sets technique, and report our results on 307 volumes of 45 patients. It was found that average precision is improved by 10% when using GBLA.