Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
The Skull Stripping Problem in MRI Solved by a Single 3D Watershed Transform
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Statistical morphological skull stripping of adult and infant MRI data
Computers in Biology and Medicine
HEAD: The Human Encephalon Automatic Delimiter
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Fuzzy-ASM Based Automated Skull Stripping Method from Infantile Brain MR Images
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
A Modified Skull-Stripping Method Based on Morphological Processing
ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 01
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This paper describes a novel automatic skull-stripping method for premature infant data. A skull-stripping approach involves the removal of non-brain tissue from medical brain images. The new method reduces the image artefacts, generates binary masks and multiple thresholds, and extracts the region of interest. To define the outer boundary of the brain tissue, a binary mask is generated using morphological operators, followed by region growing and edge detection. For a better accuracy, a threshold for each slice in the volume is calculated using k-means clustering. The segmentation of the brain tissue is achieved by applying a region growing and finalized with a local edge refinement. This technique has been tested and compared to manually segmented data and to four well-established state of the art brain extraction methods.