Brain Extraction (classification of brain and non-brain tissue) of MRI brain images is a crucial pre-processing step
necessary for imaging-based anatomical studies of the human brain. Several automated methods and software tools are
available for performing this task, but differences in MR image parameters (pulse sequence, resolution) and instrumentand
subject-dependent noise and artefacts affect the performance of these automated methods. We describe and evaluate
a method that automatically adapts the default parameters of the Brain Surface Extraction (BSE) algorithm to optimize a
cost function chosen to reflect accurate brain extraction. BSE uses a combination of anisotropic filtering, Marr-Hildreth
edge detection, and binary morphology for brain extraction. Our algorithm automatically adapts four parameters associated
with these steps to maximize the brain surface area to volume ratio. We evaluate the method on a total of 109 brain volumes
with ground truth brain masks generated by an expert user. A quantitative evaluation of the performance of the proposed
algorithm showed an improvement in the mean (s.d.) Dice coefficient from 0.8969 (0.0376) for default parameters to
0.9509 (0.0504) for the optimized case. These results indicate that automatic parameter optimization can result in
significant improvements in definition of the brain mask.
Gautham Rajagopal, Anand A. Joshi, and Richard M. Leahy, "An algorithm for automatic parameter adjustment for brain extraction in BrainSuite," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330K (Presented at SPIE Medical Imaging: February 13, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254631.
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