skimage2.morphology.diameter_closing#

skimage2.morphology.diameter_closing(image, diameter_threshold=8, connectivity=1, parent=None, tree_traverser=None)[source]#

Perform a diameter closing of the image.

Diameter closing removes all dark structures of an image with maximal extension smaller than diameter_threshold. The maximal extension is defined as the maximal extension of the bounding box. The operator is also called Bounding Box Closing. In practice, the result is similar to a morphological closing, but long and thin structures are not removed.

Technically, this operator is based on the max-tree representation of the image.

Parameters:

imagendarray: The input image for which the diameter_closing is to be calculated. This image can be of any type.
diameter_thresholdunsigned int: The maximal extension parameter (number of pixels). The default value is 8.
connectivityunsigned int, optional: The neighborhood connectivity. The integer represents the maximum number of orthogonal steps to reach a neighbor. In 2D, it is 1 for a 4-neighborhood and 2 for a 8-neighborhood. Default value is 1.
parentndarray, int64, optional: Precomputed parent image representing the max tree of the inverted image. This function is fast, if precomputed parent and tree_traverser are provided. See Note for further details.
tree_traverser1D array, int64, optional: Precomputed traverser, where the pixels are ordered such that every pixel is preceded by its parent (except for the root which has no parent). This function is fast, if precomputed parent and tree_traverser are provided. See Note for further details.

Returns:

outputndarray: Output image of the same shape and type as input image.

See also

skimage.morphology.area_opening
skimage.morphology.area_closing
skimage.morphology.diameter_opening
skimage.morphology.max_tree

Notes

If a max-tree representation (parent and tree_traverser) are given to the function, they must be calculated from the inverted image for this function, i.e.: >>> from _skimage2.morphology import max_tree >>> from _skimage2.util import invert >>> P, S = max_tree(invert(f)) >>> closed = diameter_closing(f, 3, parent=P, tree_traverser=S)

References

[1]

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

[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

Examples

We create an image (quadratic function with a minimum in the center and 4 additional local minima.

>>> w = 12
>>> x, y = np.mgrid[0:w,0:w]
>>> f = 180 + 0.2*((x - w/2)**2 + (y-w/2)**2)
>>> f[2:3,1:5] = 160; f[2:4,9:11] = 140; f[9:11,2:4] = 120
>>> f[9:10,9:11] = 100; f[10,10] = 100
>>> f = f.astype(int)

We can calculate the diameter closing:

>>> closed = diameter_closing(f, 3, connectivity=1)

All small minima with a maximal extension of 2 or less are removed. The remaining minima have all a maximal extension of at least 3.