基于有偏场的适配模糊聚类分割算法

近年来提出了许多监督和非监督模式识别技术用于磁共振图象的组织分类和定量分析。但是，这些方法的精度受到图象灰度不均匀性的严重影响。对于那些受灰度不均匀性影响的图象，如磁共振图象（MRI），提出了一种新的基于有偏场适配模糊聚类算法（BAFCM）来产生它们的分割结果。该算法通过修改C－means模糊算法中的目标方程，引入了描述灰度不均匀性的增益场，通过将增益场经为有偏场的计算，避免了AFCM中为保证增益场平滑缓慢变化引入的第一阶和第二阶正则项空间改变的二阶差分方程的复杂计算。其不仅对图象的灰度不均匀性进行了校正，而且实现了脑组织图象的快速全自动分割。

A Bias Based Adaptive Fuzzy Segmentation Algorithm

A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the tissue segmentation and quantitative analysis of magnetic resonance images. However, the accuracy of these methods is affected seriously by the intensity inhomogeneities of images. In this paper, We present a novel algorithm(BAFCM) for fuzzy segmentations of images that are subject to intensity inhomogeneities, such as magnetic resonance image. The algorithm is formulated by modifying the objective function in the fuzzy c means algorithm to include a gain field, which models image intensity inhomogeneities. First and second order regulation terms in AFCM algorithm ensure that the gain field is both slowly varying and smooth, but increase complexity of computation greatly. Instead of computing gain field, we compute bias field first, then convert bias field to gain field. With BAFCM, we can correct the intensity inhomogeneities and implement fast classification of human brain tissue of MR image automatically.