Abstract—Juxta-Vascular Nodule Segmentation Based on Flow Entropy and Geodesic Distance. Computed aided diagnosis of lung CT data is a new quantitative analysis technique to distinguish malignant nodules from benign ones. Nodule growth rate is a key indicator to < Final Year Projects > discriminate between benign and malignant nodules. Accurate nodule segmentation is the essential for calculating the nodule growth rate. However, it is difficult to segment juxta-vascular nodules, due to the similar gray levels in nodule and attached blood vessels. To distinguish the nodule region from the adjacent vessel region, a flowing direction feature, referred to as the direction of the normal vector for a pixel, is introduced. Since blood is flowing in one single direction through a vessel, the normal vectors of pixels in the vessel region typically point in similar orientations while the directions of those in the nodule region can be viewed as disorganized. The entropy value of the flowing direction features in a neighboring region for a vessel pixel is smaller than that for a nodule pixel. Moreover, vessel pixels typically have a larger geodesic distance to the nodule center than nodule pixels. Based on k -means clustering method, the flow entropy, combined with the geodesic distance, is used to segment vessel attached nodules. The validation of the proposed segmentation algorithm was carried out on juxta-vascular nodules, identified in the Chinalung-CT screening trial and on Lung Image Database Consortium (LIDC) dataset. In fully automated mode, accuracies of 92.9% (26/28), 87.5%(7/8), and 94.9% (149/157) are reached for the outlining of juxta-vascular nodules in the Chinalung-CT, and the first and second datasets of LIDC, respectively. Furthermore, it is demonstrated that the proposed method has low time complexity and high accuracies.
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