激光与光电子学进展, 2018, 55 (6): 061701, 网络出版: 2019-04-10 复制并引用该论文  本文已被引 1 次  被引通知

图 & 表

图 1. 根据相似性查找表查找边缘体素的流程图

Fig. 1. Flow chart of finding the edge of the voxel according to similarity table

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图 2. 血管中体素的拓扑约束示意图

Fig. 2. Topology constraint diagram of voxels in blood vessels

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图 3. (a)胸腔三维重建及(b)局部血管簇的提取结果

Fig. 3. (a) Three-dimensional reconstruction of thoracic cavity and (b) extracted local blood vessel cluster

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图 4. 心脏局部AABB包围盒中局部血管的提取效果

Fig. 4. Extraction of local blood vessels from cardiac local AABB bounding box

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图 5. 算法改进前后的分割效果。(a)改进前;(b)改进后

Fig. 5. Segmentation results before and after the algorithm improving. (a) Before improving; (b) after improving

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表 1相似性测度对比评价

Table1. Comparative evaluation for similarity measure

Similaritymeasure Operating Ra Rb Rc Rd Re Adjacent voxelnumbers Similar voxelnumbers Meansquareerror Beforeregistration 1189.25 1147.69 1123.43 1114.16 1101.87 26 8 Afterregistration 3487.81 3479.15 3427.48 3404.97 33871.36 26 17 Correlationcoefficient Beforeregistration 0.97152 0.97102 0.96785 0.96548 0.96229 26 12 Afterregistration 0.99241 0.99216 0.99178 0.99004 0.98459 26 18 Mattersinformation Beforeregistration -1.02594 -1.02234 -1.02119 -1.02101 -1.02078 26 11 Afterregistration -1.37748 -1.37536 -1.37493 -1.37299 -1.37179 26 15

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表 2分割精度评价

Table2. Segmentation accuracy evaluation

Figurenumber Traditional voxels growth algorithm Improved voxels growth algorithm Sensitivity Specificity Accuracy Kappa Sensitivity Specificity Accuracy Kappa 1 0.5974 0.7477 0.8785 0.7664 0.8112 0.9012 0.9214 0.8785 2 0.6348 0.6336 0.8826 0.8204 0.8105 0.9016 0.9114 0.8568 3 0.6852 0.8125 0.8789 0.7344 0.7929 0.9148 0.9322 0.8897 4 0.5374 0.7756 0.8664 0.8010 0.8188 0.9248 0.9483 0.8924 5 0.7259 0.7978 0.8997 0.8452 0.8354 0.9321 0.9324 0.8899 Averagevalue 0.6348 0.8489 0.8347 0.8327 0.8137 0.9124 0.9247 0.8814

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表 3算法改进前后的体素生长评价

Table3. Voxel growth evaluation before and after algorithm improving

Algorithms andattributes Size of boundingbox (x, y, z) Number of voxelsin volume Number ofvoxels on surface Voxels growth timeinside model /s Voxels growthtime of surface /s Automatic voxelsgrowth algorithm (28, 40, 23) 26625 2957 103.89 5.83 Semi-automatic voxelsgrowth algorithm (3, 5.3, 2) 3152 345 5.59 1.46 Improved voxelsgrowth algorithm (3, 5.3, 2) 3152 345 3.78 1.12

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任国印, 吕晓琪, 杨楠, 喻大华, 张晓峰, 周涛. 改进的体素生长算法在心脏局部血管提取中的应用[J]. 激光与光电子学进展, 2018, 55(6): 061701. Guoyin Ren, Xiaoqi Lü, Nan Yang, Dahua Yu, Xiaofeng Zhang, Tao Zhou. Application of Improved Voxels Growth Algorithm in Cardiac Local Vascular Extraction[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061701.