.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_06_manipulating_images_plot_extract_rois_statistical_maps.py>`     to download the full example code or to run this example in your browser via Binder
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_06_manipulating_images_plot_extract_rois_statistical_maps.py:


Region Extraction using a t-statistical map (3D)
================================================

This example shows how to extract regions or separate the regions
from a statistical map.

We use localizer t-statistic maps from
:func:`nilearn.datasets.fetch_neurovault_auditory_computation_task`
as an input image.

The idea is to threshold an image to get foreground objects using a
function :func:`nilearn.image.threshold_img` and extract objects using a function
:func:`nilearn.regions.connected_regions`.

Fetching t-statistic image of localizer constrasts by loading from datasets
utilities


.. code-block:: default

    from nilearn import datasets

    localizer = datasets.fetch_neurovault_auditory_computation_task()
    tmap_filename = localizer.images[0]








Threshold the t-statistic image by importing threshold function


.. code-block:: default

    from nilearn.image import threshold_img

    # Two types of strategies can be used from this threshold function
    # Type 1: strategy used will be based on scoreatpercentile
    threshold_percentile_img = threshold_img(tmap_filename, threshold='97%', copy=False)


    # Type 2: threshold strategy used will be based on image intensity
    # Here, threshold value should be within the limits i.e. less than max value.
    threshold_value_img = threshold_img(tmap_filename, threshold=3.0, copy=False)








Visualization
Showing thresholding results by importing plotting modules and its utilities


.. code-block:: default

    from nilearn import plotting

    # Showing percentile threshold image
    plotting.plot_stat_map(threshold_percentile_img, display_mode='z', cut_coords=5,
                           title='Threshold image with string percentile', colorbar=False)

    # Showing intensity threshold image
    plotting.plot_stat_map(threshold_value_img, display_mode='z', cut_coords=5,
                           title='Threshold image with intensity value', colorbar=False)




.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_001.png
          :alt: plot extract rois statistical maps
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_002.png
          :alt: plot extract rois statistical maps
          :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    <nilearn.plotting.displays.ZSlicer object at 0x7f8bfaf20370>



Extracting the regions by importing connected regions function


.. code-block:: default

    from nilearn.regions import connected_regions

    regions_percentile_img, index = connected_regions(threshold_percentile_img,
                                                      min_region_size=1500)

    regions_value_img, index = connected_regions(threshold_value_img,
                                                 min_region_size=1500)








Visualizing region extraction results


.. code-block:: default

    title = ("ROIs using percentile thresholding. "
             "\n Each ROI in same color is an extracted region")
    plotting.plot_prob_atlas(regions_percentile_img, bg_img=tmap_filename,
                             view_type='contours', display_mode='z',
                             cut_coords=5, title=title)
    title = ("ROIs using image intensity thresholding. "
             "\n Each ROI in same color is an extracted region")
    plotting.plot_prob_atlas(regions_value_img, bg_img=tmap_filename,
                             view_type='contours', display_mode='z',
                             cut_coords=5, title=title)
    plotting.show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_003.png
          :alt: plot extract rois statistical maps
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_004.png
          :alt: plot extract rois statistical maps
          :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /home/varoquau/dev/nilearn/nilearn/plotting/displays.py:97: UserWarning: No contour levels were found within the data range.
      im = getattr(ax, type)(data_2d.copy(),





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  10.009 seconds)


.. _sphx_glr_download_auto_examples_06_manipulating_images_plot_extract_rois_statistical_maps.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_extract_rois_statistical_maps.ipynb
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_extract_rois_statistical_maps.py <plot_extract_rois_statistical_maps.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_extract_rois_statistical_maps.ipynb <plot_extract_rois_statistical_maps.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_