Quantitative analysis of MR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel MR images. The method simultaneously corrects for MR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels. MS lesions are detected as voxels that are not well explained by the model. The results of the automated method are compared with the lesions delineated by human experts, showing a significant total lesion load correlation and an average overall spatial correspondence similar to that between the experts.
CITATION STYLE
Van Leemput, K., Maes, F., Bello, F., Vandermeulen, D., Colchester, A., & Suetens, P. (1999). Automated segmentation of MS lesions from multi-channel MR images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1679, pp. 11–21). Springer Verlag. https://doi.org/10.1007/10704282_2
Mendeley helps you to discover research relevant for your work.