Multiscale Opening of Conjoined Fuzzy Objects: Theory and Applications.
Multiscale Opening of Conjoined Fuzzy Objects: Theory and Applications.
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DOI：
10.1109/tfuzz.2015.2502278
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Hoffman EA
中科院分区：
文献类型：
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作者：
Saha PK;Basu S;Hoffman EA
建立了两个相连模糊对象的多尺度开(MSO)算法的理论性质，并将其推广到分离两个具有不同强度属性的相连模糊对象。还介绍了它在肺部CT成像中的动/静脉(A/V)分离和CT血管成像(CTA)中的颈动脉分割中的应用。新算法通过将模糊距离变换(FDT)这一形态特征与模糊连通性(一种拓扑学特征)相结合来处理单个连体对象的不同强度特性。该算法迭代地打开两个相连的对象，从大尺度开始，向更细的尺度发展。文中给出了该方法在猪肺实体模型动静脉分离中的应用结果。从灵敏度和特异度两个方面对该算法在患者CTA数据集上的准确性进行了定量评估，并与现有方法进行了比较。根据两个用户的颈动脉分割结果之间的体积一致性来检验算法的重复性。在患者CTA数据上的实验结果表明，该算法具有96.3%的平均准确率和95.1%的敏感度和97.5%的特异度，两个相互独立的用户分割结果的重复性高达94.2%。通过定制设计的图形界面，每个CTA数据大约需要25到35个用户指定的种子/分隔符，平均需要30分钟来完成患者CTA数据集中的颈动脉分割。
Theoretical properties of a multi-scale opening (MSO) algorithm for two conjoined fuzzy objects are established, and its extension to separating two conjoined fuzzy objects with different intensity properties is introduced. Also, its applications to artery/vein (A/V) separation in pulmonary CT imaging and carotid vessel segmentation in CT angiograms (CTAs) of patients with intracranial aneurysms are presented. The new algorithm accounts for distinct intensity properties of individual conjoined objects by combining fuzzy distance transform (FDT), a morphologic feature, with fuzzy connectivity, a topologic feature. The algorithm iteratively opens the two conjoined objects starting at large scales and progressing toward finer scales. Results of application of the method in separating arteries and veins in a physical cast phantom of a pig lung are presented. Accuracy of the algorithm is quantitatively evaluated in terms of sensitivity and specificity on patients' CTA data sets and its performance is compared with existing methods. Reproducibility of the algorithm is examined in terms of volumetric agreement between two users' carotid vessel segmentation results. Experimental results using this algorithm on patients' CTA data demonstrate a high average accuracy of 96.3% with 95.1% sensitivity and 97.5% specificity and a high reproducibility of 94.2% average agreement between segmentation results from two mutually independent users. Approximately, twenty-five to thirty-five user-specified seeds/separators are needed for each CTA data through a custom designed graphical interface requiring an average of thirty minutes to complete carotid vascular segmentation in a patient's CTA data set.