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Algorithms for fuzzy segmentation
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AI
DOI：10.1007/s100440050016.png)
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摘要
En 中文
Fuzzy segmentation is an effective way of segmenting out objects in pictures containing both random noise and shading. This is illustrated both on mathematically created pictures and on some obtained from medical imaging. A theory of fuzzy segmentation is presented. To perform fuzzy segmentation, a 'connectedness map' needs to be produced. It is demonstrated that greedy algorithms for creating such a connectedness map are faster than the previously used dynamic programming technique. Once the connectedness map is created, segmentation is completed by a simple thresholding of the connectedness map. This approach is efficacious in instances where simple thresholding of the original picture fails.
Keyword:
dynamic programming
fuzzy pattern recognition
greedy algorithms
medical imaging, segmentation
thresholding
