Measuring Brain Lesion Progression with a Supervised Tissue Classification System
- 1 January 2008
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC in Lecture Notes in Computer Science
- Vol. 11, 620-627
- https://doi.org/10.1007/978-3-540-85988-8_74
Abstract
Brain lesions, especially White Matter Lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this paper, we present a computer-assisted WML segmentation method, based on local features extracted from conventional multi-parametric Magnetic Resonance Imaging (MRI) sequences. A framework for preprocessing the temporal data by jointly equalizing histograms reduces the spatial and temporal variance of data, thereby improving the longitudinal stability of such measurements and hence the estimate of lesion progression. A Support Vector Machine (SVM) classifier trained on expert-defined WML’s is applied for lesion segmentation on each scan using the AdaBoost algorithm. Validation on a population of 23 patients from 3 different imaging sites with follow-up studies and WMLs of varying sizes, shapes and locations tests the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from an experienced neuroradiologist. The results show that our CAD-system achieves consistent lesion segmentation in the 4D data facilitating the disease monitoring.This publication has 13 references indexed in Scilit:
- The Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes Study (ACCORD-MIND): Rationale, Design, and MethodsThe American Journal of Cardiology, 2007
- MRI time series modeling of MS lesion developmentNeuroImage, 2006
- Fully automatic segmentation of white matter hyperintensities in MR images of the elderlyNeuroImage, 2005
- Advances in functional and structural MR image analysis and implementation as FSLNeuroImage, 2004
- Probabilistic segmentation of white matter lesions in MR imagingNeuroImage, 2004
- Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosisIEEE Transactions on Medical Imaging, 2002
- Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracyJournal of Magnetic Resonance Imaging, 2002
- A global optimisation method for robust affine registration of brain imagesMedical Image Analysis, 2001
- Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary resultsMagnetic Resonance Imaging, 2001
- Cerebral white matter lesions and cognitive function: The Rotterdam scan studyAnnals of Neurology, 2000