Awate Suyash P, Gee James C
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Inf Process Med Imaging. 2007;20:296-307. doi: 10.1007/978-3-540-73273-0_25.
This paper presents a novel statistical fuzzy-segmentation method for diffusion tensor (DT) images and magnetic resonance (MR) images. Typical fuzzy-segmentation schemes, e.g. those based on fuzzy-C-means (FCM), incorporate Gaussian class models which are inherently biased towards ellipsoidal clusters. Fiber bundles in DT images, however, comprise tensors that can inherently lie on more-complex manifolds. Unlike FCM-based schemes, the proposed method relies on modeling the manifolds underlying the classes by incorporating nonparametric data-driven statistical models. It produces an optimal fuzzy segmentation by maximizing a novel information-theoretic energy in a Markov-random-field framework. For DT images, the paper describes a consistent statistical technique for nonparametric modeling in Riemannian DT spaces that incorporates two very recent works. In this way, the proposed method provides uncertainties in the segmentation decisions, which stem from imaging artifacts including noise, partial voluming, and inhomogeneity. The paper shows results on synthetic and real, DT as well as MR images.
本文提出了一种用于扩散张量（DT）图像和磁共振（MR）图像的新型统计模糊分割方法。典型的模糊分割方案，例如基于模糊C均值（FCM）的方案，采用了高斯类模型，这些模型本质上倾向于椭圆形聚类。然而，DT图像中的纤维束包含本质上可能位于更复杂流形上的张量。与基于FCM的方案不同，所提出的方法通过纳入非参数数据驱动的统计模型来对类别的基础流形进行建模。它通过在马尔可夫随机场框架中最大化一种新型的信息论能量来产生最优的模糊分割。对于DT图像，本文描述了一种在黎曼DT空间中进行非参数建模的一致统计技术，该技术结合了两项非常新的研究成果。通过这种方式，所提出的方法在分割决策中提供了不确定性，这些不确定性源于包括噪声、部分容积效应和不均匀性在内的成像伪影。本文展示了在合成图像和真实图像（包括DT图像以及MR图像）上的结果。
