Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intra-scan and inter-scan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intra-scan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the EM algorithm leads to a fully automatic method that allows for more accurate segmentation of tissue types as well as better visualization of MRI data, that has proven to be effective in a study that includes more than 1000 brain scans.
CITATION STYLE
Wells, W. M., Grimson, W., Kikinis, R., & Jolesz, F. A. (1995). Adaptive segmentation of MRI data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 905, pp. 59–69). Springer Verlag. https://doi.org/10.1007/978-3-540-49197-2_7
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