Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation
Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation
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DOI：
10.1006/gmip.1996.0021
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发表时间：
1996-05-01
期刊：
影响因子：
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通讯作者：
Samarasekera, S
中科院分区：
文献类型：
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作者：
Udupa, JK;Samarasekera, S
图像本质上是模糊的，从图像中提取对象信息的方法应该尝试利用这一事实并尽可能真实地保留模糊性。在过去的图像分割研究中，缺乏由模糊连接性定义的图像元素“悬挂在一起”的概念。我们提出了一个理论的模糊对象的n维数字空间的基础上的概念的模糊连通性的图像元素，虽然我们的定义导致的问题，巨大的组合复杂性，理论结果使我们能够大大减少这一点，使我们的实用算法模糊对象提取。我们提出的算法提取一个指定的模糊对象，并确定所有的模糊对象存在于图像数据中，我们证明了实用的理论和算法在图像分割的基础上的几个实际例子都来自医学成像。(C)出版社：Academic Press，Inc.
Images are by nature fuzzy, Approaches to object information extraction from images should attempt to use this fact and retain fuzziness as realistically as possible. In past image segmentation research, the notion of ''hanging togetherness'' of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects for n-dimensional digital spaces based on a notion of fuzzy connectedness of image elements, Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically, leading us to practical algorithms for fuzzy object extraction. We present algorithms for extracting a specified fuzzy object and for identifying all fuzzy objects present in the image data, We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples all drawn from medical imaging. (C) 1996 Academic Press, Inc.