Attribute operators

Attribute operators (or connected operators) [1] is a family of contour preserving filtering operations in mathematical morphology. They can be implemented by max-trees [2], a compact hierarchical representation of the image.

Here, we show how to use diameter closing [3] [4], which is compared to morphological closing. Comparing the two results, we observe that the difference between image and morphological closing also extracts the long line. A thin but long line cannot contain the structuring element. The diameter closing stops the filling as soon as a maximal extension is reached. The line is therefore not filled and therefore not extracted by the difference.

import matplotlib.pyplot as plt
from skimage.morphology import diameter_closing
from skimage import data
from skimage.morphology import closing
from skimage.morphology import square

datasets = {
    'retina': {
        'image': data.microaneurysms(),
        'figsize': (15, 9),
        'diameter': 10,
        'vis_factor': 3,
        'title': 'Detection of microaneurysm',
    },
    'page': {
        'image': data.page(),
        'figsize': (15, 7),
        'diameter': 23,
        'vis_factor': 1,
        'title': 'Text detection',
    },
}

for dataset in datasets.values():
    # image with printed letters
    image = dataset['image']
    figsize = dataset['figsize']
    diameter = dataset['diameter']

    fig, ax = plt.subplots(2, 3, figsize=figsize)
    # Original image
    ax[0, 0].imshow(image, cmap='gray', aspect='equal', vmin=0, vmax=255)
    ax[0, 0].set_title('Original', fontsize=16)
    ax[0, 0].axis('off')

    ax[1, 0].imshow(image, cmap='gray', aspect='equal', vmin=0, vmax=255)
    ax[1, 0].set_title('Original', fontsize=16)
    ax[1, 0].axis('off')

    # Diameter closing : we remove all dark structures with a maximal
    # extension of less than <diameter> (12 or 23). I.e. in closed_attr, all
    # local minima have at least a maximal extension of <diameter>.
    closed_attr = diameter_closing(image, diameter, connectivity=2)

    # We then calculate the difference to the original image.
    tophat_attr = closed_attr - image

    ax[0, 1].imshow(closed_attr, cmap='gray', aspect='equal', vmin=0, vmax=255)
    ax[0, 1].set_title('Diameter Closing', fontsize=16)
    ax[0, 1].axis('off')

    ax[0, 2].imshow(
        dataset['vis_factor'] * tophat_attr,
        cmap='gray',
        aspect='equal',
        vmin=0,
        vmax=255,
    )
    ax[0, 2].set_title('Tophat (Difference)', fontsize=16)
    ax[0, 2].axis('off')

    # A morphological closing removes all dark structures that cannot
    # contain a structuring element of a certain size.
    closed = closing(image, square(diameter))

    # Again we calculate the difference to the original image.
    tophat = closed - image

    ax[1, 1].imshow(closed, cmap='gray', aspect='equal', vmin=0, vmax=255)
    ax[1, 1].set_title('Morphological Closing', fontsize=16)
    ax[1, 1].axis('off')

    ax[1, 2].imshow(
        dataset['vis_factor'] * tophat, cmap='gray', aspect='equal', vmin=0, vmax=255
    )
    ax[1, 2].set_title('Tophat (Difference)', fontsize=16)
    ax[1, 2].axis('off')
    fig.suptitle(dataset['title'], fontsize=18)
    fig.tight_layout(rect=(0, 0, 1, 0.88))

plt.show()

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References

[1]

Salembier, P., Oliveras, A., & Garrido, L. (1998). Antiextensive Connected Operators for Image and Sequence Processing. IEEE Transactions on Image Processing, 7(4), 555-570. DOI:10.1109/83.663500

[2]

Carlinet, E., & Geraud, T. (2014). A Comparative Review of Component Tree Computation Algorithms. IEEE Transactions on Image Processing, 23(9), 3885-3895. DOI:10.1109/TIP.2014.2336551

[3]

Vincent L., Proc. “Grayscale area openings and closings, their efficient implementation and applications”, EURASIP Workshop on Mathematical Morphology and its Applications to Signal Processing, Barcelona, Spain, pp.22-27, May 1993.

[4]

Walter, T., & Klein, J.-C. (2002). Automatic Detection of Microaneurysms in Color Fundus Images of the Human Retina by Means of the Bounding Box Closing. In A. Colosimo, P. Sirabella, A. Giuliani (Eds.), Medical Data Analysis. Lecture Notes in Computer Science, vol 2526, pp. 210-220. Springer Berlin Heidelberg. DOI:10.1007/3-540-36104-9_23