Segmentation of Juxta-vessel Pulmonary Nodule Based on Geodesic Distance Histogram

Pulmonary nodule segmentation is a crucial step for nodule diagnosis. But it is a tough work to segment the juxta-vessel nodule, as the attached vessel is similar with the nodule in density. This paper aims to segment juxta-vessel nodules by using geodesic distance histogram. First, we employ anisotropic diffusion to smooth nodule images and the preliminary reference point of threshold segmentation is located by projecting the filtered images in x, y, and z direction. Second, iterative threshold segmentation and geodesic distance transformation are utilized to obtain initial segmentation result containing nodule and vessel. Finally, the attached vessel is removed by using the characteristic of geodesic distance histogram. Two groups of data selected from lung image database consortium(LIDC) dataset(30 cases) and clinical examinations(10 cases) were employed to validate and evaluate the performance of the proposed method. With these experiments, we achieved an average overlap rate of 83.30% with 8.21% false positive rate over the radiologists' segmentations. Comparing with other reported methods, our new method demonstrated a good performance in preserving nodules' boundaries robustly.