Abstract
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.
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Wells, W.M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A. (1995). Adaptive Segmentation of MRI Data. In: Ayache, N. (eds) Computer Vision, Virtual Reality and Robotics in Medicine. CVRMed 1995. Lecture Notes in Computer Science, vol 905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49197-2_7
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DOI: https://doi.org/10.1007/978-3-540-49197-2_7
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