Bias field correction is an essential pre-processing requirement for brain tissue segmentation task. Authentic brain tissue regions are highly useful for classification and detection of abnormalities. A poor resolution magnetic resonance (MR) image is produced with irregularities in structure, abnormalities in the intensity distribution and noise during the acquisition procedure. The existing bias field correction methods do not consider the spatial information. Further, the problem of equidistant pixels while clustering is not addressed. These problems lead to poor segmentation accuracy. To solve these problems, the authors suggest a novel biased fuzzy clustering technique for the problem on hand. The basic idea is to incorporate the spatial information by altering the membership matrix of standard fuzzy C-means clustering to lower the effect of noise and intensity inhomogeneity. It also helps in improving the segmentation accuracies of the tissue regions by assigning the equidistant pixels to a single cluster. The suggested technique is validated with different modalities of brain MR images. Various evaluation indices are computed followed by the statistical analysis to justify the superiority of the suggested technique in comparison to the state-of-the-art methods.

中文翻译：

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基于模糊聚类的脑磁共振图像偏场校正新技术

偏置场校正是脑组织分割任务的基本预处理要求。真实的大脑组织区域对于异常的分类和检测非常有用。采集过程中会产生结构不规则，强度分布异常和噪声的低分辨率磁共振（MR）图像。现有的偏置场校正方法不考虑空间信息。此外，没有解决聚类时等距像素的问题。这些问题导致分割精度差。为了解决这些问题，作者提出了一种针对现有问题的新颖的偏向模糊聚类技术。基本思想是通过更改标准模糊C均值聚类的隶属度矩阵来合并空间信息，以降低噪声和强度不均匀性的影响。通过将等距像素分配给单个群集，它还有助于改善组织区域的分割精度。所建议的技术已通过脑部MR图像的不同模态得到验证。计算各种评估指标，然后进行统计分析，以证明所建议技术与最新技术方法相比的优越性。