Abstract
This paper presents an algorithm for classifying different tissue types in T1-weighted MR brain images using fuzzy segmentation. The main aim in this study is to compensate for the blurring effect on tissue boundaries due to partial volume effects. This paper is organized as follows: first, an adaptive greedy contour model has been developed to separate the intracranial volume (ICV) from the scalp and skull. Second, in order to deal with the problem of the partial volume effect, an algorithm for fuzzy segmentation is presented which has integrated fuzzy spatial affinity with statistical distributions of image intensities for each of the three tissues – cerebrospinal fluid, white matter and grey matter. This algorithm is tested on well-established simulated MR brain volumes to generate an extensive quantitative comparison with different noise levels and different slice thicknesses ranging from 1mm to 5mm. Finally, the results of this algorithm on clinical MR brain images are demonstrated.
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© 2006 Springer-Verlag Berlin Heidelberg
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Parveen, R., Ruff, C., Todd-Pokropek, A. (2006). Three Dimensional Tissue Classifications in MR Brain Images. In: Beichel, R.R., Sonka, M. (eds) Computer Vision Approaches to Medical Image Analysis. CVAMIA 2006. Lecture Notes in Computer Science, vol 4241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11889762_21
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DOI: https://doi.org/10.1007/11889762_21
Publisher Name: Springer, Berlin, Heidelberg
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