Wavelet-based image registration and segmentation framework for the quantitative evaluation of hydrocephalus

Abstract

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. Copyright © 2010 Fan Luo et al.