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Local region statistics combining multi-parameter intensity fitting module for medical image segmentation with intensity inhomogeneity and complex composition

机译：结合多参数强度拟合模块进行局部图像统计的强度不均匀和复杂成分的医学图像分割

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摘要

It is difficult to segment medical image with intensity inhomogeneity and complex composition, because most region-based modules relay on the intensity distributions. In this paper, we propose a novel method which uses local region statistics and multi-parameter intensity fitting as well. By replacing the original local region statistics with the novel local region statistics after bias field correction, the effect of intensity inhomogeneity can be eliminated. Then we devise a maximum likelihood energy function based on the distribution of each local region. Segmentation and bias field estimation can be jointly obtained by minimizing the proposed energy function. Furthermore, in order to characterize the features of each local region effectively, two parameters are used to fit the average intensity inside and outside of the counter, respectively. This can well handle the medical images with complex composition, such as larger gray difference even in the same region. Comparisons with several representative methods on synthetic and medical images demonstrate the superiority of the proposed method over other representative algorithms. (C) 2016 Elsevier Ltd. All rights reserved.
机译：由于大多数基于区域的模块都依赖于强度分布，因此难以分割具有强度不均匀性和复杂成分的医学图像。在本文中，我们提出了一种使用局部统计和多参数强度拟合的新方法。通过在偏置场校正之后用新颖的局部统计代替原始的局部统计，可以消除强度不均匀性的影响。然后根据每个局部区域的分布设计最大似然能量函数。可以通过最小化建议的能量函数来联合获得分段和偏置场估计。此外，为了有效地表征每个局部区域的特征，使用两个参数分别拟合计数器内部和外部的平均强度。这可以很好地处理具有复杂成分的医学图像，例如即使在相同区域中也具有较大的灰度差异。在合成和医学图像上与几种代表性方法的比较表明，该方法优于其他代表性算法。 （C）2016 Elsevier Ltd.保留所有权利。

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