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.. "auto_examples/segmentation/plot_chan_vese.py"
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.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_segmentation_plot_chan_vese.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_segmentation_plot_chan_vese.py:


======================
Chan-Vese Segmentation
======================

The Chan-Vese segmentation algorithm is designed to segment objects without
clearly defined boundaries. This algorithm is based on level sets that are
evolved iteratively to minimize an energy, which is defined by
weighted values corresponding to the sum of differences intensity
from the average value outside the segmented region, the sum of
differences from the average value inside the segmented region, and a
term which is dependent on the length of the boundary of the segmented
region.

This algorithm was first proposed by Tony Chan and Luminita Vese, in
a publication entitled "An Active Contour Model Without Edges" [1]_. See also
[2]_, [3]_.

This implementation of the algorithm is somewhat simplified in the
sense that the area factor 'nu' described in the original paper is not
implemented, and is only suitable for grayscale images.

Typical values for ``lambda1`` and ``lambda2`` are 1. If the 'background' is
very different from the segmented object in terms of distribution (for
example, a uniform black image with figures of varying intensity), then
these values should be different from each other.

Typical values for ``mu`` are between 0 and 1, though higher values can be
used when dealing with shapes with very ill-defined contours.

The algorithm also returns a list of values that corresponds to the
energy at each iteration. This can be used to adjust the various
parameters described above.

References
----------
.. [1] An Active Contour Model without Edges, Tony Chan and
       Luminita Vese, Scale-Space Theories in Computer Vision, 1999,
       :DOI:`10.1007/3-540-48236-9_13`
.. [2] Chan-Vese Segmentation, Pascal Getreuer, Image Processing On
       Line, 2 (2012), pp. 214-224,
       :DOI:`10.5201/ipol.2012.g-cv`
.. [3] The Chan-Vese Algorithm - Project Report, Rami Cohen, 2011
       :arXiv:`1107.2782`

.. GENERATED FROM PYTHON SOURCE LINES 46-86



.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_chan_vese_001.png
   :alt: Original Image, Chan-Vese segmentation - 200 iterations, Final Level Set, Evolution of energy over iterations
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_chan_vese_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    from skimage import data, img_as_float
    from skimage.segmentation import chan_vese

    image = img_as_float(data.camera())
    # Feel free to play around with the parameters to see how they impact the result
    cv = chan_vese(
        image,
        mu=0.25,
        lambda1=1,
        lambda2=1,
        tol=1e-3,
        max_num_iter=200,
        dt=0.5,
        init_level_set="checkerboard",
        extended_output=True,
    )

    fig, axes = plt.subplots(2, 2, figsize=(8, 8))
    ax = axes.flatten()

    ax[0].imshow(image, cmap="gray")
    ax[0].set_axis_off()
    ax[0].set_title("Original Image", fontsize=12)

    ax[1].imshow(cv[0], cmap="gray")
    ax[1].set_axis_off()
    title = f'Chan-Vese segmentation - {len(cv[2])} iterations'
    ax[1].set_title(title, fontsize=12)

    ax[2].imshow(cv[1], cmap="gray")
    ax[2].set_axis_off()
    ax[2].set_title("Final Level Set", fontsize=12)

    ax[3].plot(cv[2])
    ax[3].set_title("Evolution of energy over iterations", fontsize=12)

    fig.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 3.525 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_chan_vese.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-image/scikit-image/v0.24.0?filepath=notebooks/auto_examples/segmentation/plot_chan_vese.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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


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