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A Novel Multiresolution Fuzzy Segmentation Method on MR Image

机译:一种新的MR图像多分辨率模糊分割方法

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摘要

Multiresolution-based magnetic resonance (MR) image segmentation has attracted attention for its ability to capture rich information across scales compared with the conventional segmentation methods. In this paper, a new scale-space-based segmentation model is presented, where both the intra-scale and inter-scale properties are considered and formulated as two fuzzy energy functions. Meanwhile, a control parameter is introduced to adjust the contribution of the similarity character across scales and the clustering character within the scale. By minimizing the combined inter/intra energy function, the multiresolution fuzzy segmentation algorithm is derived. Then the coarse to fine leading segmentation is performed automatically and iteratively on a set of multiresolution images. The validity of the proposed algorithm is demonstrated by the test image and pathological MR images. Experiments show that by this approach the segmentation results, especially in the tumor area delineation, are more precise than those of the conventional fuzzy segmentation methods.
机译:与传统的分割方法相比,基于多分辨率的磁共振(MR)图像分割功能能够跨尺度捕获丰富的信息,因此备受关注。本文提出了一种新的基于尺度空间的分割模型,其中考虑了尺度内和尺度间的属性并将其表述为两个模糊能量函数。同时,引入控制参数来调整跨尺度的相似性特征和尺度内的聚类特征的贡献。通过最小化组合的内部/内部能量函数,得出了多分辨率模糊分割算法。然后,对一组多分辨率图像自动迭代地执行从粗糙到精细的前导分割。测试图像和病理MR图像证明了该算法的有效性。实验表明,这种方法的分割结果,特别是在肿瘤区域的分割中,比常规的模糊分割方法更精确。

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