Wavelet-based image registration and segmentation framework for the quantitative evaluation of hydrocephalus.
Wavelet-based image registration and segmentation framework for the quantitative evaluation of hydrocephalus.
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DOI：
10.1155/2010/248393
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发表时间：
2010
影响因子：
7.6
通讯作者：
Gregson PH
中科院分区：
文献类型：
--
作者：
Luo F;Evans JW;Linney NC;Schmidt MH;Gregson PH
脑积水的特征是脑室内液体增多，传统上通过连续 CT 扫描的视觉评估来评估。心室系统的复杂形状使得 CT 扫描的准确视觉比较变得困难。目前的研究开发了一种定量方法来测量脑室容积随时间的变化。所开发框架的关键要素是：基于互信息和小波多分辨率分析的自适应图像配准；基于双树复小波变换的自适应分割和新颖特征提取；体积计算。该框架在物理模型上进行测试时，误差为 2.3%。在对临床病例进行验证时，结果显示，被视为正常/稳定的病例的计算体积变化小于 5%。患有进行性脑积水/接受治疗的患者的计算变化大于 20%。这些发现表明该框架是合理的，并且具有作为评估脑积水的工具的潜力。
Hydrocephalus, characterized by increased fluid in the cerebral ventricles, is traditionally evaluated by a visual assessment of serial CT scans. The complex shape of the ventricular system makes accurate visual comparison of CT scans difficult. The current research developed a quantitative method to measure the change in cerebral ventricular volume over time. Key elements of the developed framework are: adaptive image registration based on mutual information and wavelet multiresolution analysis; adaptive segmentation with novel feature extraction based on the Dual-Tree Complex Wavelet Transform; volume calculation. The framework, when tested on physical phantoms, had an error of 2.3%. When validated on clinical cases, results showed that cases deemed to be normal/stable had a calculated volume change less than 5%. Those with progressive/treated hydrocephalus had a calculated change greater than 20%. These findings indicate that the framework is reasonable and has potential for development as a tool in the evaluation of hydrocephalus.