An algorithm for automatic parameter adjustment for brain extraction in BrainSuite

Gautham Rajagopal; Anand A. Joshi; Richard M. Leahy

doi:10.1117/12.2254631

24 February 2017 An algorithm for automatic parameter adjustment for brain extraction in BrainSuite

Gautham Rajagopal, Anand A. Joshi, Richard M. Leahy

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101330K (2017) https://doi.org/10.1117/12.2254631
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

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.

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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 (24 February 2017); https://doi.org/10.1117/12.2254631

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