基于水平集的医学图像分割

Gaetano Impoco
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引用次数: 0

摘要

计算机辅助诊断术前计划和术后结果评估被广泛认为是下一代外科手术的重要课题。在这种情况下,患者解剖结构的3D模型是非常有价值的。这些模型的准确性强烈依赖于在建模链的第一阶段起作用的分类和分割算法。一类很有前途的分割算法与水平集方法有关。本文简要介绍了水平集在医学图像分割中的一些应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Medical Image Segmentation using Level Sets
Computer-aided diagnosis for pre-operative planning and post-operative outcome evaluation is widely considered an important topic for next-generation surgery. 3D models of the patients’ anatomical structures can be highly valuable in this context. The accuracy of these models is strongly dependent on the classification and segmentation algorithms acting at the very first stage of the modelling chain. A promising class of segmentation algorithms is related to level set methods. Here, we briefly review some applications of level sets to medical image segmentation.
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