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Automated brain extraction from T2‐weighted magnetic resonance images
Author(s) -
Datta Sushmita,
Narayana Ponnada A.
Publication year - 2011
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.22510
Subject(s) - jaccard index , artificial intelligence , histogram , magnetic resonance imaging , computer science , pattern recognition (psychology) , sørensen–dice coefficient , similarity (geometry) , scanner , nuclear medicine , segmentation , medicine , image segmentation , radiology , image (mathematics)
Purpose: To develop and implement an automated and robust technique to extract brain from T2‐weighted images. Materials and Methods: Magnetic resonance imaging (MRI) was performed on 75 adult volunteers to acquire dual fast spin echo (FSE) images with fat‐saturation technique on a 3T Philips scanner. Histogram‐derived thresholds were derived directly from the original images followed by the application of regional labeling, regional connectivity, and mathematical morphological operations to extract brain from axial late‐echo FSE (T2‐weighted) images. The proposed technique was evaluated subjectively by an expert and quantitatively using Bland–Altman plot and Jaccard and Dice similarity measures. Results: Excellent agreement between the extracted brain volumes with the proposed technique and manual stripping by an expert was observed based on Bland–Altman plot and also as assessed by high similarity indices (Jaccard: 0.9825 ± 0.0045; Dice: 0.9912 ± 0.0023). Conclusion: Brain extraction using the proposed automated methodology is robust and the results are reproducible. J. Magn. Reson. Imaging 2011;33:822–829. © 2011 Wiley‐Liss, Inc.