Conference Proceedings
Local intensity model: An outlier detection framework with applications to white matter hyperintensity segmentation
P Raniga, P Schmitt, P Bourgeat, J Fripp, VL Villemagne, CC Rowe, O Salvado
Proceedings International Symposium on Biomedical Imaging | IEEE | Published : 2011
Abstract
Automatic segmentation of white matter hyperintensities (WMH) from T2-Weighted and FLAIR MRI is a common task that needs to be performed in the analysis of many different diseases. A method to segment the WMH is proposed whereby a local intensity model (LIM) of normal tissue is generated. WMH are detected as outliers from this model. The LIM enables an accurate modeling of intensity variations thus reducing false positives. Moreover only scans with normal tissues are required to create the model. Twelve normal scans were used to generate the LIM and validation was conducted on a set of 46 scans. Similarity indices between the proposed approach and manual segmentations were 0.590.15, 0.650.08..
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Funding Acknowledgements
Data used in this article was obtained from the AIBL study funded by the CSIRO (www.aibl.csiro.au).