ABSTRACT
Objective To observe the oral part of the facial artery and facial vein and to provide anatomical data for clinical applica-tion. Methods The origin, branches, course, diameter, position of oral part of facial artery and facial vein were observed on 32 fixed cada-ves (64 sides). Results The position relation between the facial artery and facial vein is non-constant. Measure the distance from inferior border of mandible to corner of the mouth, angulus mandibulae, mental protuberance midpoint. It is (5. 49 ± 0. 63) cm, (2. 50 ± 0. 89) cm and (6. 20 ± 1. 68) cm in the left side respectively, and (5. 69 ± 0. 72) cm, (2. 56 ± 1. 08) cm and (6. 85 ± 1. 86) cm in the right side re-spectively. The diameter of facial artery in inferior border of mandible is (0. 33 ± 0. 08) cm in the left side and (0. 38 ± 0. 07) cm in the right side;while the diameter of facial vein is (0. 40 ± 0. 12) cm in the left side and (0. 42 ± 0. 18) cm in the right side. The facial artery and facial vein are not concomitant and they are not asymmetry also. The position of superior labial artery arteries is constant, but the position of inferior labial artery arteries have more variations. Conclusion The branches, course, diameter and position of oral part of facial artery and facial vein have a number of variations. The superior labial artery arteries could be positioned more easily than inferior labial artery arter-ies. Being familiar with their distribution is of great importance for clinical application.
ABSTRACT
Objective To design an automatic segmentation algorithm for lung region abstraction from CT images in computer-aided diagnosis(CAD) of lung diseases. Methods Based on the optimal threshold segmentation, an automatic region-growing method was adopted to eliminate the trachea and bronchi, the boundary tracking algorithm was modified for background elimination and lung boundary abstraction. Then, lung boundary repair was performed to obtain a fine boundary. To reduce the sensitivity of threshold selection, an iterative process was employed to find the optimal threshold. A trachea/bronchus extraction method based on position of trachea/bronchus in previous slice was introduced, which avoided selecting seed-point by handle in region-growing. Based on previous searching direction, 8-neighborhood searching method was improved to increase its efficiency. Results Experiments with four chest CT data sets showed that this algorithm was able to abstract the lung region automatically, quickly and with better precision. Conclusion The proposed algorithm is quite efficient for automated lung segmentation in the computed-aided diagnosis of lung diseases.