Kubassova Olga
School of Computing, University of Leeds, UK.
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):593-600. doi: 10.1007/978-3-540-75757-3_72.
In this paper we present an approach for blood vessel segmentation from dynamic contrast-enhanced MRI datasets of the hand joints acquired from patients with active rheumatoid arthritis. Exclusion of the blood vessels is needed for accurate visualisation of the activation events and objective evaluation of the degree of inflammation. The segmentation technique is based on statistical modelling motivated by the physiological properties of the individual tissues, such as speed of uptake and concentration of the contrast agent; it incorporates Markov random field probabilistic framework and principal component analysis. The algorithm was tested on 60 temporal slices and has shown promising results.
在本文中,我们提出了一种从活动性类风湿关节炎患者手部关节的动态对比增强磁共振成像(MRI)数据集中进行血管分割的方法。为了准确可视化激活事件并客观评估炎症程度,需要排除血管。该分割技术基于受个体组织生理特性(如造影剂摄取速度和浓度)驱动的统计建模;它结合了马尔可夫随机场概率框架和主成分分析。该算法在60个时间切片上进行了测试,并显示出了有前景的结果。