Pulmonary Tumor Volume Delineation in PET Images using Deformable Models

Lung cancer is one of the most lethal form of cancer worldwide. The tumor present in the lungs is not static and changes its shape and position during each breathing cycle. In order to segment the tumor, the physicians manually outline the tumor on each slice. Slice by slice manual segmentation is prone to errors and causes physician fatigue. A semi-automatic method to segment and track the tumor in all the frames of PET data is proposed in this paper. The tumor is segmented from each slice of the first frame using wavelet features and support vector machine classifier. This segmented tumor, after validated by the experts is used in initialization of the contour for segmentation of the tumor in subsequent frames by the level set method. Another important contribution of this paper is setting up tumor volume obtained from the first frame as the termination condition for the level set method. The results obtained from the proposed methodology are very promising and eliminates the need for manual tumor segmentation. Our proposed technique also maintains consistent segmentation and the results obtained are not dependent on the operator as is the case in manual segmentation.