Automated detection and quantification of multiple sclerosis lesions in MR volumes of the brain

Ross Mitchell; Stephen J. Karlik; Donald H. Lee M.D.; Aaron Fenster

doi:10.1117/12.59416

1 June 1992 Automated detection and quantification of multiple sclerosis lesions in MR volumes of the brain

Ross Mitchell, Stephen J. Karlik, Donald H. Lee M.D., Aaron Fenster

Proceedings Volume 1652, Medical Imaging VI: Image Processing; (1992) https://doi.org/10.1117/12.59416
Event: Medical Imaging VI, 1992, Newport Beach, CA, United States

Abstract

MRI is the principle technique for the diagnosis of multiple sclerosis. However, manually quantifying the number and extent of lesions in MR images is arduous. Therefore, we are developing a computerized 3-D quantitative system to determine the lesions' extent and their changes in time. Our system uses proton density (PD) and T2 weighted MR volumes. A 2-D histogram showing the frequency of voxels with particular PD and T2 weighted intensities reveals that white matter, grey matter (GM), and cerebro-spinal fluid voxels correspond to distinct clusters in this histogram and can be classified on this basis. However, many true MS lesion voxels have PD and T2 weighted intensities similar to GM. Therefore, on the basis of location in the histogram alone, it is difficult to differentiate all lesions voxels from GM voxels. However, some lesions have a distinctive `peak' in the 2-D histogram which can be used to identify them successfully. Using this system it is possible to assess and monitor changes in time for these lesions. To demonstrate this ability, four MR examinations of a single chronic-progressive MS patient obtained over a 510 day period were analyzed using our system. Three-dimensional volume rendering and measurement of the results clearly shows changes in lesion shape, position, and size.

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Ross Mitchell, Stephen J. Karlik, Donald H. Lee M.D., and Aaron Fenster "Automated detection and quantification of multiple sclerosis lesions in MR volumes of the brain", Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); https://doi.org/10.1117/12.59416

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