Measuring Brain Lesion Progression with a Supervised Tissue Classification System

Authors:
Evangelia I. Zacharaki;Stathis Kanterakis;R. Nick Bryan;Christos Davatzikos
Affiliations:
Department of Radiology, University of Pennsylvania, Philadelphia, USA PA 19104;Department of Radiology, University of Pennsylvania, Philadelphia, USA PA 19104;Department of Radiology, University of Pennsylvania, Philadelphia, USA PA 19104;Department of Radiology, University of Pennsylvania, Philadelphia, USA PA 19104
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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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.