.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/06_manipulating_images/plot_extract_rois_statistical_maps.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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`. .. GENERATED FROM PYTHON SOURCE LINES 18-20 Fetching t-statistic image of localizer contrasts by loading from datasets utilities .. GENERATED FROM PYTHON SOURCE LINES 20-25 .. code-block:: default from nilearn import datasets localizer = datasets.fetch_neurovault_auditory_computation_task() tmap_filename = localizer.images[0] .. GENERATED FROM PYTHON SOURCE LINES 26-27 Threshold the t-statistic image by importing threshold function .. GENERATED FROM PYTHON SOURCE LINES 27-38 .. 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) .. GENERATED FROM PYTHON SOURCE LINES 39-41 Visualization Showing thresholding results by importing plotting modules and its utilities .. GENERATED FROM PYTHON SOURCE LINES 41-51 .. 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-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_001.png :alt: plot extract rois statistical maps :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_002.png :alt: plot extract rois statistical maps :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 52-53 Extracting the regions by importing connected regions function .. GENERATED FROM PYTHON SOURCE LINES 53-61 .. 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) .. GENERATED FROM PYTHON SOURCE LINES 62-63 Visualizing region extraction results .. GENERATED FROM PYTHON SOURCE LINES 63-74 .. 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-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_003.png :alt: plot extract rois statistical maps :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_004.png :alt: plot extract rois statistical maps :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_statistical_maps_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/nicolas/GitRepos/nilearn-fork/nilearn/plotting/displays/_axes.py:71: UserWarning: No contour levels were found within the data range. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 14.370 seconds) **Estimated memory usage:** 9 MB .. _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:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/06_manipulating_images/plot_extract_rois_statistical_maps.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_extract_rois_statistical_maps.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_extract_rois_statistical_maps.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_