Statistical intensity correction and segmentation of MRI data

William M. Wells III; W. Eric L. Grimson; Ron Kikinis; Ferenc A. Jolesz

doi:10.1117/12.185172

9 September 1994 Statistical intensity correction and segmentation of MRI data

William M. Wells III, W. Eric L. Grimson, Ron Kikinis, Ferenc A. Jolesz

Proceedings Volume 2359, Visualization in Biomedical Computing 1994; (1994) https://doi.org/10.1117/12.185172
Event: Visualization in Biomedical Computing 1994, 1994, Rochester, MN, United States

Abstract

Many applications of MRI are facilitated by segmenting the volume spanned by the imagery into the various tissue types that are present. Intensity-based classification of MR images has proven to be problematic, even when advanced techniques such as non- parametric multi-channel methods are used. A persistent difficulty has been accommodating the spatial intensity inhomogeneities that are due to the equipment. This paper describes a statistical method that uses knowledge of tissue properties and intensity inhomogeneities to correct for these intensity inhomogeneities. Use of the Expectation-Maximization algorithm leads to a method (EM segmentation) for simultaneously estimating tissue class and the correcting gain field. The algorithm iterates two components to convergence: tissue classification, and gain field estimation. The result is a powerful new technique for segmenting and correcting MR images. An implementation of the method is discussed, and results are reported for segmentation of white matter and gray matter in gradient-echo and spin-echo images. Examples are shown for axial, coronal and sagittal (surface coil) images. For a given type of acquisition, intensity variations across patients, scans, and equipment have been accommodated without manual intervention in the segmentation. In this sense, the method is fully automatic for segmenting healthy brain tissue. An accuracy assessment was made in which the method was compared to manual segmentation, and to a method based on supervised multi-variate classification, in segmenting white matter and gray matter. The method was found to be consistent with manual segmentation, and closer to manual segmentation than the supervised method.

Citation Download Citation

William M. Wells III, W. Eric L. Grimson, Ron Kikinis, and Ferenc A. Jolesz "Statistical intensity correction and segmentation of MRI data", Proc. SPIE 2359, Visualization in Biomedical Computing 1994, (9 September 1994); https://doi.org/10.1117/12.185172

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