Wang Yang, Rajapakse Jagath C
Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore.
IEEE Trans Med Imaging. 2006 Jun;25(6):804-12. doi: 10.1109/tmi.2006.875426.
This paper presents a conditional random field (CRF) approach to fuse contextual dependencies in functional magnetic resonance imaging (fMRI) data for the detection of brain activation. The interactions among both activation (activated/inactive) labels and observed data of brain voxels are unified in a probabilistic framework based on the CRF, where the interaction strength can be adaptively adjusted in terms of the data similarity of neighboring sites. Compared to earlier detection methods, including statistical parametric mapping and Markov random field, the proposed method avoids the suppression of high frequency information and relaxes the strong assumption of conditional independence of observed data. Experimental results show that the proposed approach effectively integrates contextual constraints within the detection process and robustly detects brain activities from fMRI data.
本文提出了一种条件随机场（CRF）方法，用于融合功能磁共振成像（fMRI）数据中的上下文依赖关系，以检测大脑激活。激活（激活/未激活）标签与脑体素观测数据之间的相互作用统一在基于CRF的概率框架中，其中相互作用强度可根据相邻位点的数据相似性进行自适应调整。与早期的检测方法（包括统计参数映射和马尔可夫随机场）相比，该方法避免了高频信息的抑制，并放宽了观测数据条件独立性的强假设。实验结果表明，该方法在检测过程中有效地整合了上下文约束，并能从fMRI数据中稳健地检测出大脑活动。
