Computational boundary definition by geodesic graph model

Delineation of blurry boundary from medical images is challenging in particular when the target object or region of interest is adjacent to other tissues with similar or overlapping intensity distributions. To address this challenge, we propose a graph model with adaptive global and geodesic constraints to contour the indistinct boundary from CT images. The global factor reflects the appearance affinities and better differentiates background and foreground objects. The geodesic compartment is capable to capture and highlight the thin and weak boundary information. These complementary terms are incorporated in a transductive graph model for segmentation. The model was tested on 20 low contrast CT studies of patients with non-small cell lung cancer. The segmented tumor volume was compared with manual delineations and evaluated with respect to spatial overlap and shape similarity. The experimental results and student's t-test demonstrated that considering the complementary global and geodesic factors contributed to the improved boundary definition.