Biomedical image segmentation using geometric deformable models and metaheuristics
Biomedical image segmentation using geometric deformable models and metaheuristics
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DOI：
10.1016/j.compmedimag.2013.12.005
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发表时间：
2015-07-01
影响因子：
5.7
通讯作者：
Damas, Sergio
中科院分区：
文献类型：
--
作者：
Mesejo, Pablo;Valsecchi, Andrea;Damas, Sergio
提出了一种基于混合水平集的医学图像分割方法。这种新的几何可变形模型结合了区域和边缘的信息与先验形状知识引入使用可变形注册。我们的建议包括两个阶段：培训和测试。前者意味着通过遗传算法学习水平集参数，而后者是适当的分割，其中另一个元启发式算法，在这种情况下是分散搜索，推导出形状先验。在实验比较中，这种方法显示出更好的性能比一些国家的最先进的方法时，从不同的生物医学图像模态分割解剖结构。(C)2013爱思唯尔有限公司保留所有权利。
This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region- and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities. (C) 2013 Elsevier Ltd. All rights reserved.