Li Xiang, Li Lihong, Lu Hongbing, Liang Zhengrong
Department of Radiology, State University of New York at Stony Brook, Stony Brook, New York, 11794 and Department of Radiation Oncology, Columbia University, New York, New York, 10032.
Department of Radiology, State University of New York at Stony Brook, Stony Brook, New York, 11794 and Department of Engineering Science and Physics, College of Staten Island of the City University of New York, Staten Island, New York 10314.
Med Phys. 2005 Jul;32(7Part1):2337-2345. doi: 10.1118/1.1944912.
Noise, partial volume (PV) effect, and image-intensity inhomogeneity render a challenging task for segmentation of brain magnetic resonance (MR) images. Most of the current MR image segmentation methods focus on only one or two of the above-mentioned effects. The objective of this paper is to propose a unified framework, based on the maximum a posteriori probability principle, by taking all these effects into account simultaneously in order to improve image segmentation performance. Instead of labeling each image voxel with a unique tissue type, the percentage of each voxel belonging to different tissues, which we call a mixture, is considered to address the PV effect. A Markov random field model is used to describe the noise effect by considering the nearby spatial information of the tissue mixture. The inhomogeneity effect is modeled as a bias field characterized by a zero mean Gaussian prior probability. The well-known fuzzy C-mean model is extended to define the likelihood function of the observed image. This framework reduces theoretically, under some assumptions, to the adaptive fuzzy C-mean (AFCM) algorithm proposed by Pham and Prince. Digital phantom and real clinical MR images were used to test the proposed framework. Improved performance over the AFCM algorithm was observed in a clinical environment where the inhomogeneity, noise level, and PV effect are commonly encountered.
噪声、部分容积（PV）效应和图像强度不均匀性给脑磁共振（MR）图像分割带来了一项具有挑战性的任务。当前大多数MR图像分割方法仅关注上述效应中的一两种。本文的目的是基于最大后验概率原理提出一个统一框架，通过同时考虑所有这些效应来提高图像分割性能。为了解决PV效应，我们考虑的不是用唯一的组织类型标记每个图像体素，而是每个体素属于不同组织的百分比，我们称之为混合。通过考虑组织混合的附近空间信息，使用马尔可夫随机场模型来描述噪声效应。不均匀性效应被建模为一个以零均值高斯先验概率为特征的偏置场。扩展了著名的模糊C均值模型来定义观测图像的似然函数。在某些假设下，该框架理论上简化为Pham和Prince提出的自适应模糊C均值（AFCM）算法。使用数字体模和真实临床MR图像来测试所提出的框架。在通常会遇到不均匀性、噪声水平和PV效应的临床环境中，观察到该框架相对于AFCM算法有更好的性能。
