Partial volume estimation and the fuzzy C-means algorithm [brain MRI application]

Partial volume averaging (PVA) is present in nearly all practical imaging situations, medical imaging in particular. One method that has been used to account for the effects of PVA is the fuzzy c-means algorithm (FCM). The authors propose a new method for estimating the partial volume coefficient of each class at each voxel in a given image using a Bayesian statistical model. A prior probability on the partial volume coefficients is used to reject how most voxels in the image are expected to be pure. The authors then show that the results obtained by this method are quite similar and in some cases equivalent to results obtained using FCM. Both algorithms are demonstrated on a magnetic resonance image of the brain.

几乎所有实用的成像情况下，尤其是医学成像。一种用于说明PVA影响的方法是模糊C均值算法（FCM）。作者提出了一种使用贝叶斯统计模型在给定图像中每个体素上每个类别估算每个类别的部分体积系数的新方法。部分体积系数的先验概率用于拒绝图像中的大多数体素的纯度。然后，作者表明，通过这种方法获得的结果非常相似，在某些情况下等同于使用FCM获得的结果。两种算法都在大脑的磁共振图像上证明。