You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
29 April 2005Robust fast automatic skull stripping of MRI-T2 data
The efficacy of image processing and analysis on skull stripped MR images vis-a-vis the original images is well established. Additionally, compliance with the Health Insurance Portability and Accountability Act (HIPAA) requires neuroimage repositories to anonymise the images before sharing them. This makes the non-trivial skull stripping process all the more significant. While a number of optimal approaches exist to strip the skull from T1-weighted MR images to the best of our knowledge, there is no simple, robust, fast, parameter free and fully automatic technique to perform the same on T2-weighted images. This paper presents a strategy to fill this gap. It employs a fast parameterization of the T2 image intensity onto a standardized T1 intensity scale. The parametric "T1-like" image obtained via the transformation, which takes only a few seconds to compute, is subsequently processed by any of the many T1-based brain extraction techniques to derive the brain mask. Masking the original T2 image with this brain mask strips the skull. By standardizing the intensity of the parametric image, preset algorithm-specific parameters (if any) could be used across multiple datasets. The proposed scheme has been used in a number of phantom and clinical T2 brain datasets to successfully strip the skull.