skimage.morphology.area_opening(image, area_threshold=64, connectivity=1, parent=None, tree_traverser=None) [source]
 
Perform an area opening of the image. Area opening removes all bright structures of an image with a surface smaller than area_threshold. The output image is thus the largest image smaller than the input for which all local maxima have at least a surface of area_threshold pixels. Area openings are similar to morphological openings, but they do not use a fixed structuring element, but rather a deformable one, with surface = area_threshold. Consequently, the area_opening with area_threshold=1 is the identity. In the binary case, area openings are equivalent to remove_small_objects; this operator is thus extended to gray-level images. Technically, this operator is based on the max-tree representation of the image.  Parameters 
 
imagendarray 

The input image for which the area_opening is to be calculated. This image can be of any type.  
area_thresholdunsigned int 

The size parameter (number of pixels). The default value is arbitrarily chosen to be 64.  
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 

Parent image representing the max tree of the image. The value of each pixel is the index of its parent in the ravelled array.  
tree_traverser1D array, int64, optional 

The ordered pixel indices (referring to the ravelled array). The pixels are ordered such that every pixel is preceded by its parent (except for the root which has no parent).    Returns 
 
outputndarray 

Output image of the same shape and type as the input image.      See also  
skimage.morphology.area_closing


skimage.morphology.diameter_opening


skimage.morphology.diameter_closing


skimage.morphology.max_tree


skimage.morphology.remove_small_objects


skimage.morphology.remove_small_holes

  References  
1  
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.  
2  
Soille, P., “Morphological Image Analysis: Principles and Applications” (Chapter 6), 2nd edition (2003), ISBN 3540429883. :DOI:10.1007/978-3-662-05088-0  
3  
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  
4  
Najman, L., & Couprie, M. (2006). Building the component tree in quasi-linear time. IEEE Transactions on Image Processing, 15(11), 3531-3539. :DOI:10.1109/TIP.2006.877518  
5  
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 maximum in the center and 4 additional local maxima. >>> w = 12
>>> x, y = np.mgrid[0:w,0:w]
>>> f = 20 - 0.2*((x - w/2)**2 + (y-w/2)**2)
>>> f[2:3,1:5] = 40; f[2:4,9:11] = 60; f[9:11,2:4] = 80
>>> f[9:10,9:11] = 100; f[10,10] = 100
>>> f = f.astype(int)
 We can calculate the area opening: >>> open = area_opening(f, 8, connectivity=1)
 The peaks with a surface smaller than 8 are removed.