TY - GEN
T1 - Measuring brain lesion progression with a supervised tissue classification system
AU - Zacharaki, Evangelia I.
AU - Kanterakis, Stathis
AU - Bryan, R. Nick
AU - Davatzikos, Christos
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - CAD
KW - Classification
KW - Lesion progression
KW - MRI
KW - SVM
KW - Segmentation
KW - White matter lesions
UR - http://www.scopus.com/inward/record.url?scp=79551686762&partnerID=8YFLogxK
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U2 - 10.1007/978-3-540-85988-8_74
DO - 10.1007/978-3-540-85988-8_74
M3 - Conference contribution
C2 - 18979798
AN - SCOPUS:79551686762
SN - 354085987X
SN - 9783540859871
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 620
EP - 627
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
T2 - 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
Y2 - 6 September 2008 through 10 September 2008
ER -