.. _sphx_glr_auto_examples_applications:

Longer examples and demonstrations
----------------------------------



.. raw:: html

    <div class="sphx-glr-thumbnails">

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Scikit-image currently doesn&#x27;t feature a function that allows you to write text onto an image. However, there is a fairly easy workaround using scikit-image&#x27;s optional dependency matplotlib.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_text_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_text`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Render text onto an image</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This computer vision example shows how to detect faces on an image using object detection framework based on machine learning.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_face_detection_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_face_detection`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Face detection using a cascade classifier</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we explore interactively a biomedical image which has three spatial dimensions and three color dimensions (channels). For a general introduction to 3D image processing, please refer to sphx_glr_auto_examples_applications_plot_3d_image_processing.py. The data we use here correspond to kidney tissue which was imaged with confocal fluorescence microscopy (more details at [1]_ under kidney-tissue-fluorescence.tif).">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_3d_interaction_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_3d_interaction`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Interact with 3D images (of kidney tissue)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In various image analysis situations, it is useful to think of the pixels of an image, or of a region of an image, as a network or graph, in which each pixel is connected to its neighbors (with or without diagonals). One such situation is finding the geodesic center of an object, which is the point closest to all other points if you are only allowed to travel on the pixels of the object, rather than in a straight line. This point is the one with maximal closeness centrality [1]_ in the network.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_pixel_graphs_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_pixel_graphs`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Use pixel graphs to find an object's geodesic center</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Image comparison is particularly useful when performing image processing tasks such as exposure manipulations, filtering, and restoration.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_image_comparison_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_image_comparison`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Visual image comparison</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Morphological image processing is a collection of non-linear operations related to the shape or morphology of features in an image, such as boundaries, skeletons, etc. In any given technique, we probe an image with a small shape or template called a structuring element, which defines the region of interest or neighborhood around a pixel.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_morphology_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_morphology`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Morphological Filtering</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we will see how to segment objects from a background. We use the coins image from skimage.data, which shows several coins outlined against a darker background.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_coins_segmentation_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_coins_segmentation`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Comparing edge-based and region-based segmentation</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we compute the structure tensor of a 3D image. For a general introduction to 3D image processing, please refer to sphx_glr_auto_examples_applications_plot_3d_image_processing.py. The data we use here are sampled from an image of kidney tissue obtained by confocal fluorescence microscopy (more details at [1]_ under kidney-tissue-fluorescence.tif).">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_3d_structure_tensor_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_3d_structure_tensor`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Estimate anisotropy in a 3D microscopy image</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we demonstrate the use of different metrics to assess the colocalization of two different image channels.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_colocalization_metrics_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_colocalization_metrics`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Colocalization metrics</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we analyze a microscopy image of human cells. We use data provided by Jason Moffat [1]_ through CellProfiler.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_human_mitosis_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_human_mitosis`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Segment human cells (in mitosis)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Thresholding is used to create a binary image from a grayscale image [1]_. It is the simplest way to segment objects from a background.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_thresholding_guide_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_thresholding_guide`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Thresholding</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Optical coherence tomography (OCT) is a non-invasive imaging technique used by ophthalmologists to take pictures of the back of a patient&#x27;s eye [1]_. When performing OCT, dust may stick to the reference mirror of the equipment, causing dark spots to appear on the images. The problem is that these dirt spots cover areas of in-vivo tissue, hence hiding data of interest. Our goal here is to restore (reconstruct) the hidden areas based on the pixels near their boundaries.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_cornea_spot_inpainting_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_cornea_spot_inpainting`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Restore spotted cornea image with inpainting</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we identify and track the solid-liquid (S-L) interface in a nickel-based alloy undergoing solidification. Tracking the solidification over time enables the calculation of the solidification velocity. This is important to characterize the solidified structure of the sample and will be used to inform research into additive manufacturing of metals. The image sequence was obtained by the Center for Advanced Non-Ferrous Structural Alloys (CANFSA) using synchrotron x-radiography at the Advanced Photon Source (APS) at Argonne National Laboratory (ANL). This analysis was first presented at a conference [1]_.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_solidification_tracking_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_solidification_tracking`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Track solidification of a metallic alloy</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Haar-like feature descriptors were successfully used to implement the first real-time face detector [1]_. Inspired by this application, we propose an example illustrating the extraction, selection, and classification of Haar-like features to detect faces vs. non-faces.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_haar_extraction_selection_classification_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_haar_extraction_selection_classification`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Face classification using Haar-like feature descriptor</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example reproduces a well-established workflow in bioimage data analysis for measuring the fluorescence intensity localized to the nuclear envelope, in a time sequence of cell images (each with two channels and two spatial dimensions) which shows a process of protein re-localization from the cytoplasmic area to the nuclear envelope. This biological application was first presented by Andrea Boni and collaborators in [1]_; it was used in a textbook by Kota Miura [2]_ as well as in other works ([3]_, [4]_). In other words, we port this workflow from ImageJ Macro to Python with scikit-image.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_fluorescence_nuclear_envelope_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_fluorescence_nuclear_envelope`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Measure fluorescence intensity at the nuclear envelope</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial is an introduction to three-dimensional image processing. For a quick intro to 3D datasets, please refer to sphx_glr_auto_examples_data_plot_3d.py. Images are represented as numpy arrays. A single-channel, or grayscale, image is a 2D matrix of pixel intensities of shape (n_row, n_col), where n_row (resp. n_col) denotes the number of rows (resp. columns). We can construct a 3D volume as a series of 2D planes, giving 3D images the shape (n_plane, n_row, n_col), where n_plane is the number of planes. A multichannel, or RGB(A), image has an additional channel dimension in the final position containing color information.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_3d_image_processing_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_3d_image_processing`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Explore 3D images (of cells)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Rank filters are non-linear filters using local gray-level ordering to compute the filtered value. This ensemble of filters share a common base: the local gray-level histogram is computed on the neighborhood of a pixel (defined by a 2D structuring element). If the filtered value is taken as the middle value of the histogram, we get the classical median filter.">

.. only:: html

  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_rank_filters_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_rank_filters`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Rank filters</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_text
   /auto_examples/applications/plot_face_detection
   /auto_examples/applications/plot_3d_interaction
   /auto_examples/applications/plot_pixel_graphs
   /auto_examples/applications/plot_image_comparison
   /auto_examples/applications/plot_morphology
   /auto_examples/applications/plot_coins_segmentation
   /auto_examples/applications/plot_3d_structure_tensor
   /auto_examples/applications/plot_colocalization_metrics
   /auto_examples/applications/plot_human_mitosis
   /auto_examples/applications/plot_thresholding_guide
   /auto_examples/applications/plot_cornea_spot_inpainting
   /auto_examples/applications/plot_solidification_tracking
   /auto_examples/applications/plot_haar_extraction_selection_classification
   /auto_examples/applications/plot_fluorescence_nuclear_envelope
   /auto_examples/applications/plot_3d_image_processing
   /auto_examples/applications/plot_rank_filters