749855e9d1b5bd1aa7b46db144503922fbc0a336,doc/examples/brain_extraction_dwi.py,,,#,8

Before Change

         dipy_home+"/datasets_multi-site_all_companies/1.5T/Siemens/b0.nii.gz",
         dipy_home+"/datasets_multi-site_all_companies/Stanford/b0.nii.gz"]

for f in files :
    print(f)
    img = nib.load(f)
    data = img.get_data()
    fname = os.path.splitext(os.path.splitext(f)[0])[0]

    b0_mask, mask = medotsu(data, 4, 4, autocrop=False)
    mask_img = nib.Nifti1Image(mask.astype(np.float32), img.get_affine())
    b0_img = nib.Nifti1Image(b0_mask.astype(np.float32), img.get_affine())
    nib.save(mask_img, fname+"_binary_mask.nii.gz")
    nib.save(b0_img, fname+"_mask.nii.gz")
    
    b0_mask_crop, mask_crop = medotsu(data, 4, 4, autocrop=True)
    mask_img_crop = nib.Nifti1Image(mask_crop.astype(np.float32), img.get_affine())
    b0_img_crop = nib.Nifti1Image(b0_mask_crop.astype(np.float32), img.get_affine())
    nib.save(mask_img_crop, fname+"_binary_mask_crop.nii.gz")
    nib.save(b0_img_crop, fname+"_mask_crop.nii.gz")

After Change

plt.imshow(data[:,:,slice])
plt.subplot(1,2,2)
plt.imshow(b0_mask[:,:,slice])
plt.show()


`medostu` can also automaticaly crop the outputs to remove the largest possible
number of backgroud voxels. This makes outputted data significantly smaller.

Instances