Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images

Brian Johnston; M. Stella Atkins; Kellogg S. Booth

doi:10.1117/12.175075

11 May 1994 Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images

Brian Johnston, M. Stella Atkins, Kellogg S. Booth

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175075
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

Abstract

An important first step in diagnosis and treatment planning using tomographic imaging is differentiating and quantifying diseased as well as healthy tissue. One of the difficulties encountered in solving this problem to date has been distinguishing the partial volume constituents of each voxel in the image volume. Most proposed solutions to this problem involve analysis of planar images, in sequence, in two dimensions only. We have extended a model-based method of image segmentation which applies the technique of iterated conditional modes in three dimensions. A minimum of user intervention is required to train the algorithm. Partial volume estimates for each voxel in the image are obtained yielding fractional compositions of multiple tissue types for individual voxels. A multispectral approach is applied, where spatially registered data sets are available. The algorithm is simple and has been parallelized using a dataflow programming environment to reduce the computational burden. The algorithm has been used to segment dual echo MRI data sets of multiple sclerosis patients using lesions, gray matter, white matter, and cerebrospinal fluid as the partial volume constituents. The results of the application of the algorithm to these datasets is presented and compared to the manual lesion segmentation of the same data.

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Brian Johnston, M. Stella Atkins, and Kellogg S. Booth "Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175075

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