基于多尺度FNM建模和MRF松弛标记的无监督医学图像分析

Yang Wang, T. Adalı, T. Lei
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引用次数: 3

摘要

通过结合局部上下文,我们导出了两种类型的像素图像分块FNM模型。然后将自学习表述为信息匹配问题,通过首先估计模型参数初始化ML解,然后通过MRF松弛进行更精细的分割来解决。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised medical image analysis by multiscale FNM modeling and MRF relaxation labeling
We derive two types of block-wise FNM model for pixel images by incorporating local context. The self-learning is then formulated as an information match problem and solved by first estimating model parameters to initialize ML solution and then conducting finer segmentation through MRF relaxation.
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