基于流向特征熵和测地线距离的粘连血管型肺结节聚类分割
摘要
Computed aided diagnosis (CAD) of lung CT is a new quantitative analysis imaging technique to distinguish malignant nodules from benign ones. Nodule growth rate is a key indicator for judgment of benign or malignant nodule. Accurate nodule segmentation is the premise condition of calculating nodule growth rate. However, it is difficult to segment Juxta-vascular nodules, due to similar gray levels between nodule and vessel. To distinguish the nodule region from the adjacent vessel region, a flowing direction feature, referred to as the direction of normal vector for a pixel, is introduced. Since all blood has the same flowing direction through a vessel, the normal vectors of pixels in the vessel region typically point to similar orientations while the directions of those in the nodule region can be viewed as disorganized. So the entropy value of flowing direction features in a neighbor region for a vessel pixel is bigger than that for a nodule pixel. And vessel pixels typically have a larger geodesic distance to the nodule center than nodule pixels. Based on k-Means clustering method, the flowing entropy feature, combined with geodesic distance feature, is proposed to solve the segmentation problem of the vessel attachment nodule. The validation of the proposed segmentation algorithm was carried out on Juxta-vascular nodules (104 solid nodule and 28 Juxta-vascular nodule), identified in the Chinalung-CT screening trail and on the lung image database consortium (LIDC) dataset. Among them, there are 12 nodules in the first LIDC database (4 solid nodules and 8 Juxta-vascular nodules) and 182 nodules in the second LIDC database (25 solid nodules and 157 Juxta-vascular nodules). Comparison is done between the gold standard and experimental results. The correct segmentations on solid nodule are 100/104(96.2%), 4/4(100%) and 24/25(96.0%), respectively, while the correct segmentations on Juxta-vascular nodule are 26/28(92.9%),7/8(87.5%) and 149/157(94.9%), respectively, show