.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/segmentation/plot_morphsnakes.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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_morphsnakes.py: ==================== Morphological Snakes ==================== *Morphological Snakes* [1]_ are a family of methods for image segmentation. Their behavior is similar to that of active contours (for example, *Geodesic Active Contours* [2]_ or *Active Contours without Edges* [3]_). However, *Morphological Snakes* use morphological operators (such as dilation or erosion) over a binary array instead of solving PDEs over a floating point array, which is the standard approach for active contours. This makes *Morphological Snakes* faster and numerically more stable than their traditional counterpart. There are two *Morphological Snakes* methods available in this implementation: *Morphological Geodesic Active Contours* (**MorphGAC**, implemented in the function ``morphological_geodesic_active_contour``) and *Morphological Active Contours without Edges* (**MorphACWE**, implemented in the function ``morphological_chan_vese``). **MorphGAC** is suitable for images with visible contours, even when these contours might be noisy, cluttered, or partially unclear. It requires, however, that the image is preprocessed to highlight the contours. This can be done using the function ``inverse_gaussian_gradient``, although the user might want to define their own version. The quality of the **MorphGAC** segmentation depends greatly on this preprocessing step. On the contrary, **MorphACWE** works well when the pixel values of the inside and the outside regions of the object to segment have different averages. Unlike **MorphGAC**, **MorphACWE** does not require that the contours of the object are well defined, and it works over the original image without any preceding processing. This makes **MorphACWE** easier to use and tune than **MorphGAC**. References ---------- .. [1] A Morphological Approach to Curvature-based Evolution of Curves and Surfaces, Pablo Márquez-Neila, Luis Baumela and Luis Álvarez. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014, :DOI:`10.1109/TPAMI.2013.106` .. [2] Geodesic Active Contours, Vicent Caselles, Ron Kimmel and Guillermo Sapiro. In International Journal of Computer Vision (IJCV), 1997, :DOI:`10.1023/A:1007979827043` .. [3] Active Contours without Edges, Tony Chan and Luminita Vese. In IEEE Transactions on Image Processing, 2001, :DOI:`10.1109/83.902291` .. GENERATED FROM PYTHON SOURCE LINES 49-137 .. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_morphsnakes_001.png :alt: Morphological ACWE segmentation, Morphological ACWE evolution, Morphological GAC segmentation, Morphological GAC evolution :srcset: /auto_examples/segmentation/images/sphx_glr_plot_morphsnakes_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/scikit-image/scikit-image/doc/examples/segmentation/plot_morphsnakes.py:92: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later. /home/runner/work/scikit-image/scikit-image/doc/examples/segmentation/plot_morphsnakes.py:94: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later. /home/runner/work/scikit-image/scikit-image/doc/examples/segmentation/plot_morphsnakes.py:96: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later. /home/runner/work/scikit-image/scikit-image/doc/examples/segmentation/plot_morphsnakes.py:126: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later. /home/runner/work/scikit-image/scikit-image/doc/examples/segmentation/plot_morphsnakes.py:128: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later. /home/runner/work/scikit-image/scikit-image/doc/examples/segmentation/plot_morphsnakes.py:130: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later. | .. code-block:: default import numpy as np import matplotlib.pyplot as plt from skimage import data, img_as_float from skimage.segmentation import (morphological_chan_vese, morphological_geodesic_active_contour, inverse_gaussian_gradient, checkerboard_level_set) def store_evolution_in(lst): """Returns a callback function to store the evolution of the level sets in the given list. """ def _store(x): lst.append(np.copy(x)) return _store # Morphological ACWE image = img_as_float(data.camera()) # Initial level set init_ls = checkerboard_level_set(image.shape, 6) # List with intermediate results for plotting the evolution evolution = [] callback = store_evolution_in(evolution) ls = morphological_chan_vese(image, num_iter=35, init_level_set=init_ls, smoothing=3, iter_callback=callback) 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].contour(ls, [0.5], colors='r') ax[0].set_title("Morphological ACWE segmentation", fontsize=12) ax[1].imshow(ls, cmap="gray") ax[1].set_axis_off() contour = ax[1].contour(evolution[2], [0.5], colors='g') contour.collections[0].set_label("Iteration 2") contour = ax[1].contour(evolution[7], [0.5], colors='y') contour.collections[0].set_label("Iteration 7") contour = ax[1].contour(evolution[-1], [0.5], colors='r') contour.collections[0].set_label("Iteration 35") ax[1].legend(loc="upper right") title = "Morphological ACWE evolution" ax[1].set_title(title, fontsize=12) # Morphological GAC image = img_as_float(data.coins()) gimage = inverse_gaussian_gradient(image) # Initial level set init_ls = np.zeros(image.shape, dtype=np.int8) init_ls[10:-10, 10:-10] = 1 # List with intermediate results for plotting the evolution evolution = [] callback = store_evolution_in(evolution) ls = morphological_geodesic_active_contour(gimage, num_iter=230, init_level_set=init_ls, smoothing=1, balloon=-1, threshold=0.69, iter_callback=callback) ax[2].imshow(image, cmap="gray") ax[2].set_axis_off() ax[2].contour(ls, [0.5], colors='r') ax[2].set_title("Morphological GAC segmentation", fontsize=12) ax[3].imshow(ls, cmap="gray") ax[3].set_axis_off() contour = ax[3].contour(evolution[0], [0.5], colors='g') contour.collections[0].set_label("Iteration 0") contour = ax[3].contour(evolution[100], [0.5], colors='y') contour.collections[0].set_label("Iteration 100") contour = ax[3].contour(evolution[-1], [0.5], colors='r') contour.collections[0].set_label("Iteration 230") ax[3].legend(loc="upper right") title = "Morphological GAC evolution" ax[3].set_title(title, fontsize=12) fig.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.953 seconds) .. _sphx_glr_download_auto_examples_segmentation_plot_morphsnakes.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/v0.22.x?filepath=notebooks/auto_examples/segmentation/plot_morphsnakes.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_morphsnakes.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_morphsnakes.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_