title: 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.
authors.affiliation: Mathematics and Computing Science Department, Saint Mary's University, Halifax, NS, Canada B3H 3C3.
authors.forename: Fan
authors.initials: F
authors.lastname: Luo
authors.forename: Jeanette W
authors.initials: JW
authors.lastname: Evans
authors.forename: Norma C
authors.initials: NC
authors.lastname: Linney
authors.forename: Matthias H
authors.initials: MH
authors.lastname: Schmidt
authors.forename: Peter H
authors.initials: PH
authors.lastname: Gregson