ENH: Drop FSL BET to estimate the "outskin" (head) mask #1105
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This PR contributes toward #1032 by eliminating the need for FSL BET. By using an implementation closer to the gradient-based of (Mortamet, 2009), there is no more need for BET.
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NiPype interfaces are broken as of dipy 1.6.x (nipy/nipype#3555). Since we do not necessarily need a particular dipy version (other than above 1.4), it's just easier to cap dipy at 1.5.
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Also install numpy to build dipy.
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This PR contributes toward #1032 by eliminating the need for FSL BET. This implementation requires Dipy for the nonlinear means denoising.
BTW - the nonlinear means denoising could be leveraged to calculate the sigma of the snr calculations more precisely, obtaining much better estimates.
By using an implementation closer to the gradient-based of (Mortamet, 2009), there is no more need for BET.