NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask voxels is necessary. Use case: working with time series of resting-state or task maps.
This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.
Parameters
| Parameter | Value |
|---|---|
| detrend | False |
| dtype | None |
| high_pass | None |
| low_pass | None |
| mask_args | None |
| mask_img | None |
| mask_strategy | background |
| memory | Memory(location=None) |
| memory_level | 1 |
| reports | True |
| sample_mask | None |
| sessions | None |
| smoothing_fwhm | None |
| standardize | False |
| t_r | None |
| target_affine | None |
| target_shape | None |
| verbose | 0 |
Computing a mask from raw EPI data ############################################################################## From raw EPI data, there is no uniform background, and a different strategy is necessary .. code-block:: default # Load movie watching based brain development fmri dataset dataset = datasets.fetch_development_fmri(n_subjects=1) epi_filename = dataset.func[0] # Restrict to 100 frames to speed up computation from nilearn.image import index_img epi_img = index_img(epi_filename, slice(0, 100)) # To display the background mean_img = image.mean_img(epi_img) plot_epi(mean_img, title='Mean EPI image') .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_004.png :alt: plot mask computation :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none
NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask voxels is necessary. Use case: working with time series of resting-state or task maps.
This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.
Parameters
| Parameter | Value |
|---|---|
| detrend | False |
| dtype | None |
| high_pass | None |
| low_pass | None |
| mask_args | None |
| mask_img | None |
| mask_strategy | epi |
| memory | Memory(location=None) |
| memory_level | 1 |
| reports | True |
| sample_mask | None |
| sessions | None |
| smoothing_fwhm | None |
| standardize | False |
| t_r | None |
| target_affine | None |
| target_shape | None |
| verbose | 0 |
Generate mask with strong opening We can fine-tune the outline of the mask by increasing the number of opening steps (`opening=10`) using the `mask_args` argument of the NiftiMasker. This effectively performs erosion and dilation operations on the outer voxel layers of the mask, which can for example remove remaining skull parts in the image. .. code-block:: default masker = NiftiMasker(mask_strategy='epi', mask_args=dict(opening=10)) masker.fit(epi_img) report = masker.generate_report() report .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_006.png :alt: plot mask computation :class: sphx-glr-single-img .. only:: builder_html .. raw:: html
NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask voxels is necessary. Use case: working with time series of resting-state or task maps.
This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.
Parameters
| Parameter | Value |
|---|---|
| detrend | False |
| dtype | None |
| high_pass | None |
| low_pass | None |
| mask_args | {'opening': 10} |
| mask_img | None |
| mask_strategy | epi |
| memory | Memory(location=None) |
| memory_level | 1 |
| reports | True |
| sample_mask | None |
| sessions | None |
| smoothing_fwhm | None |
| standardize | False |
| t_r | None |
| target_affine | None |
| target_shape | None |
| verbose | 0 |
Generate mask with a high lower cutoff The NiftiMasker calls the nilearn.masking.compute_epi_mask function to compute the mask from the EPI. It has two important parameters: lower_cutoff and upper_cutoff. These set the grey-value bounds in which the masking algorithm will search for its threshold (0 being the minimum of the image and 1 the maximum). We will here increase the lower cutoff to enforce selection of those voxels that appear as bright in the EPI image. .. code-block:: default masker = NiftiMasker(mask_strategy='epi', mask_args=dict(upper_cutoff=.9, lower_cutoff=.8, opening=False)) masker.fit(epi_img) report = masker.generate_report() report .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_007.png :alt: plot mask computation :class: sphx-glr-single-img .. only:: builder_html .. raw:: html
NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask voxels is necessary. Use case: working with time series of resting-state or task maps.
This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.
Parameters
| Parameter | Value |
|---|---|
| detrend | False |
| dtype | None |
| high_pass | None |
| low_pass | None |
| mask_args | {'upper_cutoff': 0.9, 'lower_cutoff': 0.8, 'opening': False} |
| mask_img | None |
| mask_strategy | epi |
| memory | Memory(location=None) |
| memory_level | 1 |
| reports | True |
| sample_mask | None |
| sessions | None |
| smoothing_fwhm | None |
| standardize | False |
| t_r | None |
| target_affine | None |
| target_shape | None |
| verbose | 0 |
Computing the mask from the MNI template ############################################################################## A mask can also be computed from the MNI gray matter template. In this case, it is resampled to the target image .. code-block:: default masker = NiftiMasker(mask_strategy='template') masker.fit(epi_img) report = masker.generate_report() report .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_008.png :alt: plot mask computation :class: sphx-glr-single-img .. only:: builder_html .. raw:: html
NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask voxels is necessary. Use case: working with time series of resting-state or task maps.
This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image.
Parameters
| Parameter | Value |
|---|---|
| detrend | False |
| dtype | None |
| high_pass | None |
| low_pass | None |
| mask_args | None |
| mask_img | None |
| mask_strategy | template |
| memory | Memory(location=None) |
| memory_level | 1 |
| reports | True |
| sample_mask | None |
| sessions | None |
| smoothing_fwhm | None |
| standardize | False |
| t_r | None |
| target_affine | None |
| target_shape | None |
| verbose | 0 |
Compute and resample a mask ############################################################################## NiftiMasker also allows passing parameters directly to `image.resample_img`. We can specify a `target_affine`, a `target_shape`, or both. For more information on these arguments, see :doc:`plot_affine_transformation`. The NiftiMasker report allows us to see the mask before and after resampling. Simply hover over the report to see the mask from the original image. .. code-block:: default import numpy as np masker = NiftiMasker(mask_strategy='epi', target_affine=np.eye(3) * 8) masker.fit(epi_img) report = masker.generate_report() report .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_009.png :alt: plot mask computation :class: sphx-glr-multi-img * .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_mask_computation_010.png :alt: plot mask computation :class: sphx-glr-multi-img .. only:: builder_html .. raw:: html
NiftiMasker Applying a mask to extract time-series from Niimg-like objects. NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask voxels is necessary. Use case: working with time series of resting-state or task maps.
This report shows the input Nifti image overlaid with the outlines of the mask (in green). We recommend to inspect the report for the overlap between the mask and its input image. To see the input Nifti image before resampling, hover over the displayed image.
Parameters
| Parameter | Value |
|---|---|
| detrend | False |
| dtype | None |
| high_pass | None |
| low_pass | None |
| mask_args | None |
| mask_img | None |
| mask_strategy | epi |
| memory | Memory(location=None) |
| memory_level | 1 |
| reports | True |
| sample_mask | None |
| sessions | None |
| smoothing_fwhm | None |
| standardize | False |
| t_r | None |
| target_affine | [[8. 0. 0.] [0. 8. 0.] [0. 0. 8.]] |
| target_shape | None |
| verbose | 0 |
After mask computation: extracting time series ############################################################################## Extract time series .. code-block:: default # trended vs detrended trended = NiftiMasker(mask_strategy='epi') detrended = NiftiMasker(mask_strategy='epi', detrend=True) trended_data = trended.fit_transform(epi_img) detrended_data = detrended.fit_transform(epi_img) # The timeseries are numpy arrays, so we can manipulate them with numpy print("Trended: mean %.2f, std %.2f" % (np.mean(trended_data), np.std(trended_data))) print("Detrended: mean %.2f, std %.2f" % (np.mean(detrended_data), np.std(detrended_data))) show() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Trended: mean 552.82, std 168.28 Detrended: mean -0.00, std 5.88 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 11.840 seconds) .. _sphx_glr_download_auto_examples_06_manipulating_images_plot_mask_computation.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/master?filepath=examples/auto_examples/06_manipulating_images/plot_mask_computation.ipynb :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_mask_computation.py