Module: segmentation  
skimage.segmentation.active_contour(image, snake) Active contour model.  
skimage.segmentation.chan_vese(image[, mu, …]) Chan-Vese segmentation algorithm.  
skimage.segmentation.checkerboard_level_set(…) Create a checkerboard level set with binary values.  
skimage.segmentation.circle_level_set(…[, …]) Create a circle level set with binary values.  
skimage.segmentation.clear_border(labels[, …]) Clear objects connected to the label image border.  
skimage.segmentation.disk_level_set(…[, …]) Create a disk level set with binary values.  
skimage.segmentation.expand_labels(label_image) Expand labels in label image by distance pixels without overlapping.  
skimage.segmentation.felzenszwalb(image[, …]) Computes Felsenszwalb’s efficient graph based image segmentation.  
skimage.segmentation.find_boundaries(label_img) Return bool array where boundaries between labeled regions are True.  
skimage.segmentation.flood(image, seed_point, *) Mask corresponding to a flood fill.  
skimage.segmentation.flood_fill(image, …) Perform flood filling on an image.  
skimage.segmentation.inverse_gaussian_gradient(image) Inverse of gradient magnitude.  
skimage.segmentation.join_segmentations(s1, s2) Return the join of the two input segmentations.  
skimage.segmentation.mark_boundaries(image, …) Return image with boundaries between labeled regions highlighted.  
skimage.segmentation.morphological_chan_vese(…) Morphological Active Contours without Edges (MorphACWE)  
skimage.segmentation.morphological_geodesic_active_contour(…) Morphological Geodesic Active Contours (MorphGAC).  
skimage.segmentation.quickshift(image[, …]) Segments image using quickshift clustering in Color-(x,y) space.  
skimage.segmentation.random_walker(data, labels) Random walker algorithm for segmentation from markers.  
skimage.segmentation.relabel_sequential(…) Relabel arbitrary labels to {offset, …  
skimage.segmentation.slic(image[, …]) Segments image using k-means clustering in Color-(x,y,z) space.  
skimage.segmentation.watershed(image[, …]) Find watershed basins in image flooded from given markers.   active_contour  
skimage.segmentation.active_contour(image, snake, alpha=0.01, beta=0.1, w_line=0, w_edge=1, gamma=0.01, max_px_move=1.0, max_iterations=2500, convergence=0.1, *, boundary_condition='periodic', coordinates='rc') [source]
 
Active contour model. Active contours by fitting snakes to features of images. Supports single and multichannel 2D images. Snakes can be periodic (for segmentation) or have fixed and/or free ends. The output snake has the same length as the input boundary. As the number of points is constant, make sure that the initial snake has enough points to capture the details of the final contour.  Parameters 
 
image(N, M) or (N, M, 3) ndarray 

Input image.  
snake(N, 2) ndarray 

Initial snake coordinates. For periodic boundary conditions, endpoints must not be duplicated.  
alphafloat, optional 

Snake length shape parameter. Higher values makes snake contract faster.  
betafloat, optional 

Snake smoothness shape parameter. Higher values makes snake smoother.  
w_linefloat, optional 

Controls attraction to brightness. Use negative values to attract toward dark regions.  
w_edgefloat, optional 

Controls attraction to edges. Use negative values to repel snake from edges.  
gammafloat, optional 

Explicit time stepping parameter.  
max_px_movefloat, optional 

Maximum pixel distance to move per iteration.  
max_iterationsint, optional 

Maximum iterations to optimize snake shape.  
convergencefloat, optional 

Convergence criteria.  
boundary_conditionstring, optional 

Boundary conditions for the contour. Can be one of ‘periodic’, ‘free’, ‘fixed’, ‘free-fixed’, or ‘fixed-free’. ‘periodic’ attaches the two ends of the snake, ‘fixed’ holds the end-points in place, and ‘free’ allows free movement of the ends. ‘fixed’ and ‘free’ can be combined by parsing ‘fixed-free’, ‘free-fixed’. Parsing ‘fixed-fixed’ or ‘free-free’ yields same behaviour as ‘fixed’ and ‘free’, respectively.  
coordinates{‘rc’}, optional 

This option remains for compatibility purpose only and has no effect. It was introduced in 0.16 with the 'xy' option, but since 0.18, only the 'rc' option is valid. Coordinates must be set in a row-column format.    Returns 
 
snake(N, 2) ndarray 

Optimised snake, same shape as input parameter.     References  
1  
Kass, M.; Witkin, A.; Terzopoulos, D. “Snakes: Active contour models”. International Journal of Computer Vision 1 (4): 321 (1988). DOI:10.1007/BF00133570   Examples >>> from skimage.draw import circle_perimeter
>>> from skimage.filters import gaussian
 Create and smooth image: >>> img = np.zeros((100, 100))
>>> rr, cc = circle_perimeter(35, 45, 25)
>>> img[rr, cc] = 1
>>> img = gaussian(img, 2)
 Initialize spline: >>> s = np.linspace(0, 2*np.pi, 100)
>>> init = 50 * np.array([np.sin(s), np.cos(s)]).T + 50
 Fit spline to image: >>> snake = active_contour(img, init, w_edge=0, w_line=1, coordinates='rc')  
>>> dist = np.sqrt((45-snake[:, 0])**2 + (35-snake[:, 1])**2)  
>>> int(np.mean(dist))  
25
 
 chan_vese  
skimage.segmentation.chan_vese(image, mu=0.25, lambda1=1.0, lambda2=1.0, tol=0.001, max_iter=500, dt=0.5, init_level_set='checkerboard', extended_output=False) [source]
 
Chan-Vese segmentation algorithm. Active contour model by evolving a level set. Can be used to segment objects without clearly defined boundaries.  Parameters 
 
image(M, N) ndarray 

Grayscale image to be segmented.  
mufloat, optional 

‘edge length’ weight parameter. Higher mu values will produce a ‘round’ edge, while values closer to zero will detect smaller objects.  
lambda1float, optional 

‘difference from average’ weight parameter for the output region with value ‘True’. If it is lower than lambda2, this region will have a larger range of values than the other.  
lambda2float, optional 

‘difference from average’ weight parameter for the output region with value ‘False’. If it is lower than lambda1, this region will have a larger range of values than the other.  
tolfloat, positive, optional 

Level set variation tolerance between iterations. If the L2 norm difference between the level sets of successive iterations normalized by the area of the image is below this value, the algorithm will assume that the solution was reached.  
max_iteruint, optional 

Maximum number of iterations allowed before the algorithm interrupts itself.  
dtfloat, optional 

A multiplication factor applied at calculations for each step, serves to accelerate the algorithm. While higher values may speed up the algorithm, they may also lead to convergence problems.  
init_level_setstr or (M, N) ndarray, optional 

Defines the starting level set used by the algorithm. If a string is inputted, a level set that matches the image size will automatically be generated. Alternatively, it is possible to define a custom level set, which should be an array of float values, with the same shape as ‘image’. Accepted string values are as follows.  ‘checkerboard’

the starting level set is defined as sin(x/5*pi)*sin(y/5*pi), where x and y are pixel coordinates. This level set has fast convergence, but may fail to detect implicit edges.  ‘disk’

the starting level set is defined as the opposite of the distance from the center of the image minus half of the minimum value between image width and image height. This is somewhat slower, but is more likely to properly detect implicit edges.  ‘small disk’

the starting level set is defined as the opposite of the distance from the center of the image minus a quarter of the minimum value between image width and image height.    
extended_outputbool, optional 

If set to True, the return value will be a tuple containing the three return values (see below). If set to False which is the default value, only the ‘segmentation’ array will be returned.    Returns 
 
segmentation(M, N) ndarray, bool 

Segmentation produced by the algorithm.  
phi(M, N) ndarray of floats 

Final level set computed by the algorithm.  
energieslist of floats 

Shows the evolution of the ‘energy’ for each step of the algorithm. This should allow to check whether the algorithm converged.     Notes The Chan-Vese 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]. 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 ‘energy’ which this algorithm tries to minimize is defined as the sum of the differences from the average within the region squared and weighed by the ‘lambda’ factors to which is added the length of the contour multiplied by the ‘mu’ factor. Supports 2D grayscale images only, and does not implement the area term described in the original article. 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   
 checkerboard_level_set  
skimage.segmentation.checkerboard_level_set(image_shape, square_size=5) [source]
 
Create a checkerboard level set with binary values.  Parameters 
 
image_shapetuple of positive integers 

Shape of the image.  
square_sizeint, optional 

Size of the squares of the checkerboard. It defaults to 5.    Returns 
 
outarray with shape image_shape 

Binary level set of the checkerboard.      See also  
circle_level_set

  
 circle_level_set  
skimage.segmentation.circle_level_set(image_shape, center=None, radius=None) [source]
 
Create a circle level set with binary values.  Parameters 
 
image_shapetuple of positive integers 

Shape of the image  
centertuple of positive integers, optional 

Coordinates of the center of the circle given in (row, column). If not given, it defaults to the center of the image.  
radiusfloat, optional 

Radius of the circle. If not given, it is set to the 75% of the smallest image dimension.    Returns 
 
outarray with shape image_shape 

Binary level set of the circle with the given radius and center.    Warns 
 Deprecated:

 New in version 0.17: This function is deprecated and will be removed in scikit-image 0.19. Please use the function named disk_level_set instead.       See also  
checkerboard_level_set

  
 clear_border  
skimage.segmentation.clear_border(labels, buffer_size=0, bgval=0, in_place=False, mask=None) [source]
 
Clear objects connected to the label image border.  Parameters 
 
labels(M[, N[, …, P]]) array of int or bool 

Imaging data labels.  
buffer_sizeint, optional 

The width of the border examined. By default, only objects that touch the outside of the image are removed.  
bgvalfloat or int, optional 

Cleared objects are set to this value.  
in_placebool, optional 

Whether or not to manipulate the labels array in-place.  
maskndarray of bool, same shape as image, optional. 

Image data mask. Objects in labels image overlapping with False pixels of mask will be removed. If defined, the argument buffer_size will be ignored.    Returns 
 
out(M[, N[, …, P]]) array 

Imaging data labels with cleared borders     Examples >>> import numpy as np
>>> from skimage.segmentation import clear_border
>>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
...                    [1, 1, 0, 0, 1, 0, 0, 1, 0],
...                    [1, 1, 0, 1, 0, 1, 0, 0, 0],
...                    [0, 0, 0, 1, 1, 1, 1, 0, 0],
...                    [0, 1, 1, 1, 1, 1, 1, 1, 0],
...                    [0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> clear_border(labels)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 1, 0, 0, 0, 0],
       [0, 0, 0, 1, 0, 1, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 1, 0, 0],
       [0, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> mask = np.array([[0, 0, 1, 1, 1, 1, 1, 1, 1],
...                  [0, 0, 1, 1, 1, 1, 1, 1, 1],
...                  [1, 1, 1, 1, 1, 1, 1, 1, 1],
...                  [1, 1, 1, 1, 1, 1, 1, 1, 1],
...                  [1, 1, 1, 1, 1, 1, 1, 1, 1],
...                  [1, 1, 1, 1, 1, 1, 1, 1, 1]]).astype(bool)
>>> clear_border(labels, mask=mask)
array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 1, 0, 0, 1, 0],
       [0, 0, 0, 1, 0, 1, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 1, 0, 0],
       [0, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0]])
 
 disk_level_set  
skimage.segmentation.disk_level_set(image_shape, *, center=None, radius=None) [source]
 
Create a disk level set with binary values.  Parameters 
 
image_shapetuple of positive integers 

Shape of the image  
centertuple of positive integers, optional 

Coordinates of the center of the disk given in (row, column). If not given, it defaults to the center of the image.  
radiusfloat, optional 

Radius of the disk. If not given, it is set to the 75% of the smallest image dimension.    Returns 
 
outarray with shape image_shape 

Binary level set of the disk with the given radius and center.      See also  
checkerboard_level_set

  
 expand_labels  
skimage.segmentation.expand_labels(label_image, distance=1) [source]
 
Expand labels in label image by distance pixels without overlapping. Given a label image, expand_labels grows label regions (connected components) outwards by up to distance pixels without overflowing into neighboring regions. More specifically, each background pixel that is within Euclidean distance of <= distance pixels of a connected component is assigned the label of that connected component. Where multiple connected components are within distance pixels of a background pixel, the label value of the closest connected component will be assigned (see Notes for the case of multiple labels at equal distance).  Parameters 
 
label_imagendarray of dtype int 

label image  
distancefloat 

Euclidean distance in pixels by which to grow the labels. Default is one.    Returns 
 
enlarged_labelsndarray of dtype int 

Labeled array, where all connected regions have been enlarged      See also  
skimage.measure.label(), skimage.segmentation.watershed(), skimage.morphology.dilation()

  Notes Where labels are spaced more than distance pixels are apart, this is equivalent to a morphological dilation with a disc or hyperball of radius distance. However, in contrast to a morphological dilation, expand_labels will not expand a label region into a neighboring region. This implementation of expand_labels is derived from CellProfiler [1], where it is known as module “IdentifySecondaryObjects (Distance-N)” [2]. There is an important edge case when a pixel has the same distance to multiple regions, as it is not defined which region expands into that space. Here, the exact behavior depends on the upstream implementation of scipy.ndimage.distance_transform_edt. References  
1  
https://cellprofiler.org  
2  
https://github.com/CellProfiler/CellProfiler/blob/082930ea95add7b72243a4fa3d39ae5145995e9c/cellprofiler/modules/identifysecondaryobjects.py#L559   Examples >>> labels = np.array([0, 1, 0, 0, 0, 0, 2])
>>> expand_labels(labels, distance=1)
array([1, 1, 1, 0, 0, 2, 2])
 Labels will not overwrite each other: >>> expand_labels(labels, distance=3)
array([1, 1, 1, 1, 2, 2, 2])
 In case of ties, behavior is undefined, but currently resolves to the label closest to (0,) * ndim in lexicographical order. >>> labels_tied = np.array([0, 1, 0, 2, 0])
>>> expand_labels(labels_tied, 1)
array([1, 1, 1, 2, 2])
>>> labels2d = np.array(
...     [[0, 1, 0, 0],
...      [2, 0, 0, 0],
...      [0, 3, 0, 0]]
... )
>>> expand_labels(labels2d, 1)
array([[2, 1, 1, 0],
       [2, 2, 0, 0],
       [2, 3, 3, 0]])
 
 felzenszwalb  
skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20, multichannel=True) [source]
 
Computes Felsenszwalb’s efficient graph based image segmentation. Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter scale sets an observation level. Higher scale means less and larger segments. sigma is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation. The number of produced segments as well as their size can only be controlled indirectly through scale. Segment size within an image can vary greatly depending on local contrast. For RGB images, the algorithm uses the euclidean distance between pixels in color space.  Parameters 
 
image(width, height, 3) or (width, height) ndarray 

Input image.  
scalefloat 

Free parameter. Higher means larger clusters.  
sigmafloat 

Width (standard deviation) of Gaussian kernel used in preprocessing.  
min_sizeint 

Minimum component size. Enforced using postprocessing.  
multichannelbool, optional (default: True) 

Whether the last axis of the image is to be interpreted as multiple channels. A value of False, for a 3D image, is not currently supported.    Returns 
 
segment_mask(width, height) ndarray 

Integer mask indicating segment labels.     Notes The k parameter used in the original paper renamed to scale here. References  
1  
Efficient graph-based image segmentation, Felzenszwalb, P.F. and Huttenlocher, D.P. International Journal of Computer Vision, 2004   Examples >>> from skimage.segmentation import felzenszwalb
>>> from skimage.data import coffee
>>> img = coffee()
>>> segments = felzenszwalb(img, scale=3.0, sigma=0.95, min_size=5)
 
 find_boundaries  
skimage.segmentation.find_boundaries(label_img, connectivity=1, mode='thick', background=0) [source]
 
Return bool array where boundaries between labeled regions are True.  Parameters 
 
label_imgarray of int or bool 

An array in which different regions are labeled with either different integers or boolean values.  
connectivityint in {1, …, label_img.ndim}, optional 

A pixel is considered a boundary pixel if any of its neighbors has a different label. connectivity controls which pixels are considered neighbors. A connectivity of 1 (default) means pixels sharing an edge (in 2D) or a face (in 3D) will be considered neighbors. A connectivity of label_img.ndim means pixels sharing a corner will be considered neighbors.  
modestring in {‘thick’, ‘inner’, ‘outer’, ‘subpixel’} 

How to mark the boundaries:  thick: any pixel not completely surrounded by pixels of the same label (defined by connectivity) is marked as a boundary. This results in boundaries that are 2 pixels thick. inner: outline the pixels just inside of objects, leaving background pixels untouched. outer: outline pixels in the background around object boundaries. When two objects touch, their boundary is also marked. subpixel: return a doubled image, with pixels between the original pixels marked as boundary where appropriate.   
backgroundint, optional 

For modes ‘inner’ and ‘outer’, a definition of a background label is required. See mode for descriptions of these two.    Returns 
 
boundariesarray of bool, same shape as label_img 

A bool image where True represents a boundary pixel. For mode equal to ‘subpixel’, boundaries.shape[i] is equal to 2 * label_img.shape[i] - 1 for all i (a pixel is inserted in between all other pairs of pixels).     Examples >>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...                    [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
...                    [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
>>> find_boundaries(labels, mode='thick').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
       [0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
       [0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels, mode='inner').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
       [0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels, mode='outer').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
       [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
       [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
       [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
       [0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> labels_small = labels[::2, ::3]
>>> labels_small
array([[0, 0, 0, 0],
       [0, 0, 5, 0],
       [0, 1, 5, 0],
       [0, 0, 5, 0],
       [0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels_small, mode='subpixel').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 0],
       [0, 0, 0, 1, 0, 1, 0],
       [0, 1, 1, 1, 0, 1, 0],
       [0, 1, 0, 1, 0, 1, 0],
       [0, 1, 1, 1, 0, 1, 0],
       [0, 0, 0, 1, 0, 1, 0],
       [0, 0, 0, 1, 1, 1, 0],
       [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> bool_image = np.array([[False, False, False, False, False],
...                        [False, False, False, False, False],
...                        [False, False,  True,  True,  True],
...                        [False, False,  True,  True,  True],
...                        [False, False,  True,  True,  True]],
...                       dtype=bool)
>>> find_boundaries(bool_image)
array([[False, False, False, False, False],
       [False, False,  True,  True,  True],
       [False,  True,  True,  True,  True],
       [False,  True,  True, False, False],
       [False,  True,  True, False, False]])
 
 flood  
skimage.segmentation.flood(image, seed_point, *, selem=None, connectivity=None, tolerance=None) [source]
 
Mask corresponding to a flood fill. Starting at a specific seed_point, connected points equal or within tolerance of the seed value are found.  Parameters 
 
imagendarray 

An n-dimensional array.  
seed_pointtuple or int 

The point in image used as the starting point for the flood fill. If the image is 1D, this point may be given as an integer.  
selemndarray, optional 

A structuring element used to determine the neighborhood of each evaluated pixel. It must contain only 1’s and 0’s, have the same number of dimensions as image. If not given, all adjacent pixels are considered as part of the neighborhood (fully connected).  
connectivityint, optional 

A number used to determine the neighborhood of each evaluated pixel. Adjacent pixels whose squared distance from the center is larger or equal to connectivity are considered neighbors. Ignored if selem is not None.  
tolerancefloat or int, optional 

If None (default), adjacent values must be strictly equal to the initial value of image at seed_point. This is fastest. If a value is given, a comparison will be done at every point and if within tolerance of the initial value will also be filled (inclusive).    Returns 
 
maskndarray 

A Boolean array with the same shape as image is returned, with True values for areas connected to and equal (or within tolerance of) the seed point. All other values are False.     Notes The conceptual analogy of this operation is the ‘paint bucket’ tool in many raster graphics programs. This function returns just the mask representing the fill. If indices are desired rather than masks for memory reasons, the user can simply run numpy.nonzero on the result, save the indices, and discard this mask. Examples >>> from skimage.morphology import flood
>>> image = np.zeros((4, 7), dtype=int)
>>> image[1:3, 1:3] = 1
>>> image[3, 0] = 1
>>> image[1:3, 4:6] = 2
>>> image[3, 6] = 3
>>> image
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 0, 2, 2, 0],
       [0, 1, 1, 0, 2, 2, 0],
       [1, 0, 0, 0, 0, 0, 3]])
 Fill connected ones with 5, with full connectivity (diagonals included): >>> mask = flood(image, (1, 1))
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [5, 0, 0, 0, 0, 0, 3]])
 Fill connected ones with 5, excluding diagonal points (connectivity 1): >>> mask = flood(image, (1, 1), connectivity=1)
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [1, 0, 0, 0, 0, 0, 3]])
 Fill with a tolerance: >>> mask = flood(image, (0, 0), tolerance=1)
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[5, 5, 5, 5, 5, 5, 5],
       [5, 5, 5, 5, 2, 2, 5],
       [5, 5, 5, 5, 2, 2, 5],
       [5, 5, 5, 5, 5, 5, 3]])
 
 Examples using skimage.segmentation.flood
 
  Flood Fill   flood_fill  
skimage.segmentation.flood_fill(image, seed_point, new_value, *, selem=None, connectivity=None, tolerance=None, in_place=False, inplace=None) [source]
 
Perform flood filling on an image. Starting at a specific seed_point, connected points equal or within tolerance of the seed value are found, then set to new_value.  Parameters 
 
imagendarray 

An n-dimensional array.  
seed_pointtuple or int 

The point in image used as the starting point for the flood fill. If the image is 1D, this point may be given as an integer.  
new_valueimage type 

New value to set the entire fill. This must be chosen in agreement with the dtype of image.  
selemndarray, optional 

A structuring element used to determine the neighborhood of each evaluated pixel. It must contain only 1’s and 0’s, have the same number of dimensions as image. If not given, all adjacent pixels are considered as part of the neighborhood (fully connected).  
connectivityint, optional 

A number used to determine the neighborhood of each evaluated pixel. Adjacent pixels whose squared distance from the center is less than or equal to connectivity are considered neighbors. Ignored if selem is not None.  
tolerancefloat or int, optional 

If None (default), adjacent values must be strictly equal to the value of image at seed_point to be filled. This is fastest. If a tolerance is provided, adjacent points with values within plus or minus tolerance from the seed point are filled (inclusive).  
in_placebool, optional 

If True, flood filling is applied to image in place. If False, the flood filled result is returned without modifying the input image (default).  
inplacebool, optional 

This parameter is deprecated and will be removed in version 0.19.0 in favor of in_place. If True, flood filling is applied to image inplace. If False, the flood filled result is returned without modifying the input image (default).    Returns 
 
filledndarray 

An array with the same shape as image is returned, with values in areas connected to and equal (or within tolerance of) the seed point replaced with new_value.     Notes The conceptual analogy of this operation is the ‘paint bucket’ tool in many raster graphics programs. Examples >>> from skimage.morphology import flood_fill
>>> image = np.zeros((4, 7), dtype=int)
>>> image[1:3, 1:3] = 1
>>> image[3, 0] = 1
>>> image[1:3, 4:6] = 2
>>> image[3, 6] = 3
>>> image
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 0, 2, 2, 0],
       [0, 1, 1, 0, 2, 2, 0],
       [1, 0, 0, 0, 0, 0, 3]])
 Fill connected ones with 5, with full connectivity (diagonals included): >>> flood_fill(image, (1, 1), 5)
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [5, 0, 0, 0, 0, 0, 3]])
 Fill connected ones with 5, excluding diagonal points (connectivity 1): >>> flood_fill(image, (1, 1), 5, connectivity=1)
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [1, 0, 0, 0, 0, 0, 3]])
 Fill with a tolerance: >>> flood_fill(image, (0, 0), 5, tolerance=1)
array([[5, 5, 5, 5, 5, 5, 5],
       [5, 5, 5, 5, 2, 2, 5],
       [5, 5, 5, 5, 2, 2, 5],
       [5, 5, 5, 5, 5, 5, 3]])
 
 Examples using skimage.segmentation.flood_fill
 
  Flood Fill   inverse_gaussian_gradient  
skimage.segmentation.inverse_gaussian_gradient(image, alpha=100.0, sigma=5.0) [source]
 
Inverse of gradient magnitude. Compute the magnitude of the gradients in the image and then inverts the result in the range [0, 1]. Flat areas are assigned values close to 1, while areas close to borders are assigned values close to 0. This function or a similar one defined by the user should be applied over the image as a preprocessing step before calling morphological_geodesic_active_contour.  Parameters 
 
image(M, N) or (L, M, N) array 

Grayscale image or volume.  
alphafloat, optional 

Controls the steepness of the inversion. A larger value will make the transition between the flat areas and border areas steeper in the resulting array.  
sigmafloat, optional 

Standard deviation of the Gaussian filter applied over the image.    Returns 
 
gimage(M, N) or (L, M, N) array 

Preprocessed image (or volume) suitable for morphological_geodesic_active_contour.     
 join_segmentations  
skimage.segmentation.join_segmentations(s1, s2) [source]
 
Return the join of the two input segmentations. The join J of S1 and S2 is defined as the segmentation in which two voxels are in the same segment if and only if they are in the same segment in both S1 and S2.  Parameters 
 
s1, s2numpy arrays 

s1 and s2 are label fields of the same shape.    Returns 
 
jnumpy array 

The join segmentation of s1 and s2.     Examples >>> from skimage.segmentation import join_segmentations
>>> s1 = np.array([[0, 0, 1, 1],
...                [0, 2, 1, 1],
...                [2, 2, 2, 1]])
>>> s2 = np.array([[0, 1, 1, 0],
...                [0, 1, 1, 0],
...                [0, 1, 1, 1]])
>>> join_segmentations(s1, s2)
array([[0, 1, 3, 2],
       [0, 5, 3, 2],
       [4, 5, 5, 3]])
 
 mark_boundaries  
skimage.segmentation.mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None, mode='outer', background_label=0) [source]
 
Return image with boundaries between labeled regions highlighted.  Parameters 
 
image(M, N[, 3]) array 

Grayscale or RGB image.  
label_img(M, N) array of int 

Label array where regions are marked by different integer values.  
colorlength-3 sequence, optional 

RGB color of boundaries in the output image.  
outline_colorlength-3 sequence, optional 

RGB color surrounding boundaries in the output image. If None, no outline is drawn.  
modestring in {‘thick’, ‘inner’, ‘outer’, ‘subpixel’}, optional 

The mode for finding boundaries.  
background_labelint, optional 

Which label to consider background (this is only useful for modes inner and outer).    Returns 
 
marked(M, N, 3) array of float 

An image in which the boundaries between labels are superimposed on the original image.      See also  
find_boundaries

  
 Examples using skimage.segmentation.mark_boundaries
 
  Trainable segmentation using local features and random forests   morphological_chan_vese  
skimage.segmentation.morphological_chan_vese(image, iterations, init_level_set='checkerboard', smoothing=1, lambda1=1, lambda2=1, iter_callback=<function <lambda>>) [source]
 
Morphological Active Contours without Edges (MorphACWE) Active contours without edges implemented with morphological operators. It can be used to segment objects in images and volumes without well defined borders. It is required that the inside of the object looks different on average than the outside (i.e., the inner area of the object should be darker or lighter than the outer area on average).  Parameters 
 
image(M, N) or (L, M, N) array 

Grayscale image or volume to be segmented.  
iterationsuint 

Number of iterations to run  
init_level_setstr, (M, N) array, or (L, M, N) array 

Initial level set. If an array is given, it will be binarized and used as the initial level set. If a string is given, it defines the method to generate a reasonable initial level set with the shape of the image. Accepted values are ‘checkerboard’ and ‘circle’. See the documentation of checkerboard_level_set and circle_level_set respectively for details about how these level sets are created.  
smoothinguint, optional 

Number of times the smoothing operator is applied per iteration. Reasonable values are around 1-4. Larger values lead to smoother segmentations.  
lambda1float, optional 

Weight parameter for the outer region. If lambda1 is larger than lambda2, the outer region will contain a larger range of values than the inner region.  
lambda2float, optional 

Weight parameter for the inner region. If lambda2 is larger than lambda1, the inner region will contain a larger range of values than the outer region.  
iter_callbackfunction, optional 

If given, this function is called once per iteration with the current level set as the only argument. This is useful for debugging or for plotting intermediate results during the evolution.    Returns 
 
out(M, N) or (L, M, N) array 

Final segmentation (i.e., the final level set)      See also  
circle_level_set, checkerboard_level_set

  Notes This is a version of the Chan-Vese algorithm that uses morphological operators instead of solving a partial differential equation (PDE) for the evolution of the contour. The set of morphological operators used in this algorithm are proved to be infinitesimally equivalent to the Chan-Vese PDE (see [1]). However, morphological operators are do not suffer from the numerical stability issues typically found in PDEs (it is not necessary to find the right time step for the evolution), and are computationally faster. The algorithm and its theoretical derivation are described in [1]. References  
1(1,2)  
A Morphological Approach to Curvature-based Evolution of Curves and Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014, DOI:10.1109/TPAMI.2013.106   
 morphological_geodesic_active_contour  
skimage.segmentation.morphological_geodesic_active_contour(gimage, iterations, init_level_set='circle', smoothing=1, threshold='auto', balloon=0, iter_callback=<function <lambda>>) [source]
 
Morphological Geodesic Active Contours (MorphGAC). Geodesic active contours implemented with morphological operators. It can be used to segment objects with visible but noisy, cluttered, broken borders.  Parameters 
 
gimage(M, N) or (L, M, N) array 

Preprocessed image or volume to be segmented. This is very rarely the original image. Instead, this is usually a preprocessed version of the original image that enhances and highlights the borders (or other structures) of the object to segment. morphological_geodesic_active_contour will try to stop the contour evolution in areas where gimage is small. See morphsnakes.inverse_gaussian_gradient as an example function to perform this preprocessing. Note that the quality of morphological_geodesic_active_contour might greatly depend on this preprocessing.  
iterationsuint 

Number of iterations to run.  
init_level_setstr, (M, N) array, or (L, M, N) array 

Initial level set. If an array is given, it will be binarized and used as the initial level set. If a string is given, it defines the method to generate a reasonable initial level set with the shape of the image. Accepted values are ‘checkerboard’ and ‘circle’. See the documentation of checkerboard_level_set and circle_level_set respectively for details about how these level sets are created.  
smoothinguint, optional 

Number of times the smoothing operator is applied per iteration. Reasonable values are around 1-4. Larger values lead to smoother segmentations.  
thresholdfloat, optional 

Areas of the image with a value smaller than this threshold will be considered borders. The evolution of the contour will stop in this areas.  
balloonfloat, optional 

Balloon force to guide the contour in non-informative areas of the image, i.e., areas where the gradient of the image is too small to push the contour towards a border. A negative value will shrink the contour, while a positive value will expand the contour in these areas. Setting this to zero will disable the balloon force.  
iter_callbackfunction, optional 

If given, this function is called once per iteration with the current level set as the only argument. This is useful for debugging or for plotting intermediate results during the evolution.    Returns 
 
out(M, N) or (L, M, N) array 

Final segmentation (i.e., the final level set)      See also  
inverse_gaussian_gradient, circle_level_set, checkerboard_level_set

  Notes This is a version of the Geodesic Active Contours (GAC) algorithm that uses morphological operators instead of solving partial differential equations (PDEs) for the evolution of the contour. The set of morphological operators used in this algorithm are proved to be infinitesimally equivalent to the GAC PDEs (see [1]). However, morphological operators are do not suffer from the numerical stability issues typically found in PDEs (e.g., it is not necessary to find the right time step for the evolution), and are computationally faster. The algorithm and its theoretical derivation are described in [1]. References  
1(1,2)  
A Morphological Approach to Curvature-based Evolution of Curves and Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014, DOI:10.1109/TPAMI.2013.106   
 quickshift  
skimage.segmentation.quickshift(image, ratio=1.0, kernel_size=5, max_dist=10, return_tree=False, sigma=0, convert2lab=True, random_seed=42) [source]
 
Segments image using quickshift clustering in Color-(x,y) space. Produces an oversegmentation of the image using the quickshift mode-seeking algorithm.  Parameters 
 
image(width, height, channels) ndarray 

Input image.  
ratiofloat, optional, between 0 and 1 

Balances color-space proximity and image-space proximity. Higher values give more weight to color-space.  
kernel_sizefloat, optional 

Width of Gaussian kernel used in smoothing the sample density. Higher means fewer clusters.  
max_distfloat, optional 

Cut-off point for data distances. Higher means fewer clusters.  
return_treebool, optional 

Whether to return the full segmentation hierarchy tree and distances.  
sigmafloat, optional 

Width for Gaussian smoothing as preprocessing. Zero means no smoothing.  
convert2labbool, optional 

Whether the input should be converted to Lab colorspace prior to segmentation. For this purpose, the input is assumed to be RGB.  
random_seedint, optional 

Random seed used for breaking ties.    Returns 
 
segment_mask(width, height) ndarray 

Integer mask indicating segment labels.     Notes The authors advocate to convert the image to Lab color space prior to segmentation, though this is not strictly necessary. For this to work, the image must be given in RGB format. References  
1  
Quick shift and kernel methods for mode seeking, Vedaldi, A. and Soatto, S. European Conference on Computer Vision, 2008   
 random_walker  
skimage.segmentation.random_walker(data, labels, beta=130, mode='cg_j', tol=0.001, copy=True, multichannel=False, return_full_prob=False, spacing=None, *, prob_tol=0.001) [source]
 
Random walker algorithm for segmentation from markers. Random walker algorithm is implemented for gray-level or multichannel images.  Parameters 
 
dataarray_like 

Image to be segmented in phases. Gray-level data can be two- or three-dimensional; multichannel data can be three- or four- dimensional (multichannel=True) with the highest dimension denoting channels. Data spacing is assumed isotropic unless the spacing keyword argument is used.  
labelsarray of ints, of same shape as data without channels dimension 

Array of seed markers labeled with different positive integers for different phases. Zero-labeled pixels are unlabeled pixels. Negative labels correspond to inactive pixels that are not taken into account (they are removed from the graph). If labels are not consecutive integers, the labels array will be transformed so that labels are consecutive. In the multichannel case, labels should have the same shape as a single channel of data, i.e. without the final dimension denoting channels.  
betafloat, optional 

Penalization coefficient for the random walker motion (the greater beta, the more difficult the diffusion).  
modestring, available options {‘cg’, ‘cg_j’, ‘cg_mg’, ‘bf’} 

Mode for solving the linear system in the random walker algorithm.  ‘bf’ (brute force): an LU factorization of the Laplacian is computed. This is fast for small images (<1024x1024), but very slow and memory-intensive for large images (e.g., 3-D volumes). ‘cg’ (conjugate gradient): the linear system is solved iteratively using the Conjugate Gradient method from scipy.sparse.linalg. This is less memory-consuming than the brute force method for large images, but it is quite slow. ‘cg_j’ (conjugate gradient with Jacobi preconditionner): the Jacobi preconditionner is applyed during the Conjugate gradient method iterations. This may accelerate the convergence of the ‘cg’ method. ‘cg_mg’ (conjugate gradient with multigrid preconditioner): a preconditioner is computed using a multigrid solver, then the solution is computed with the Conjugate Gradient method. This mode requires that the pyamg module is installed.   
tolfloat, optional 

Tolerance to achieve when solving the linear system using the conjugate gradient based modes (‘cg’, ‘cg_j’ and ‘cg_mg’).  
copybool, optional 

If copy is False, the labels array will be overwritten with the result of the segmentation. Use copy=False if you want to save on memory.  
multichannelbool, optional 

If True, input data is parsed as multichannel data (see ‘data’ above for proper input format in this case).  
return_full_probbool, optional 

If True, the probability that a pixel belongs to each of the labels will be returned, instead of only the most likely label.  
spacingiterable of floats, optional 

Spacing between voxels in each spatial dimension. If None, then the spacing between pixels/voxels in each dimension is assumed 1.  
prob_tolfloat, optional 

Tolerance on the resulting probability to be in the interval [0, 1]. If the tolerance is not satisfied, a warning is displayed.    Returns 
 
outputndarray 

 If return_full_prob is False, array of ints of same shape and data type as labels, in which each pixel has been labeled according to the marker that reached the pixel first by anisotropic diffusion. If return_full_prob is True, array of floats of shape (nlabels, labels.shape). output[label_nb, i, j] is the probability that label label_nb reaches the pixel (i, j) first.       See also  
skimage.morphology.watershed


watershed segmentation A segmentation algorithm based on mathematical morphology and “flooding” of regions from markers.    Notes Multichannel inputs are scaled with all channel data combined. Ensure all channels are separately normalized prior to running this algorithm. The spacing argument is specifically for anisotropic datasets, where data points are spaced differently in one or more spatial dimensions. Anisotropic data is commonly encountered in medical imaging. The algorithm was first proposed in [1]. The algorithm solves the diffusion equation at infinite times for sources placed on markers of each phase in turn. A pixel is labeled with the phase that has the greatest probability to diffuse first to the pixel. The diffusion equation is solved by minimizing x.T L x for each phase, where L is the Laplacian of the weighted graph of the image, and x is the probability that a marker of the given phase arrives first at a pixel by diffusion (x=1 on markers of the phase, x=0 on the other markers, and the other coefficients are looked for). Each pixel is attributed the label for which it has a maximal value of x. The Laplacian L of the image is defined as:  L_ii = d_i, the number of neighbors of pixel i (the degree of i) L_ij = -w_ij if i and j are adjacent pixels  The weight w_ij is a decreasing function of the norm of the local gradient. This ensures that diffusion is easier between pixels of similar values. When the Laplacian is decomposed into blocks of marked and unmarked pixels: L = M B.T
    B A
 with first indices corresponding to marked pixels, and then to unmarked pixels, minimizing x.T L x for one phase amount to solving: A x = - B x_m
 where x_m = 1 on markers of the given phase, and 0 on other markers. This linear system is solved in the algorithm using a direct method for small images, and an iterative method for larger images. References  
1  
Leo Grady, Random walks for image segmentation, IEEE Trans Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83. DOI:10.1109/TPAMI.2006.233.   Examples >>> np.random.seed(0)
>>> a = np.zeros((10, 10)) + 0.2 * np.random.rand(10, 10)
>>> a[5:8, 5:8] += 1
>>> b = np.zeros_like(a, dtype=np.int32)
>>> b[3, 3] = 1  # Marker for first phase
>>> b[6, 6] = 2  # Marker for second phase
>>> random_walker(a, b)
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
       [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
       [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)
 
 relabel_sequential  
skimage.segmentation.relabel_sequential(label_field, offset=1) [source]
 
Relabel arbitrary labels to {offset, … offset + number_of_labels}. This function also returns the forward map (mapping the original labels to the reduced labels) and the inverse map (mapping the reduced labels back to the original ones).  Parameters 
 
label_fieldnumpy array of int, arbitrary shape 

An array of labels, which must be non-negative integers.  
offsetint, optional 

The return labels will start at offset, which should be strictly positive.    Returns 
 
relabelednumpy array of int, same shape as label_field 

The input label field with labels mapped to {offset, …, number_of_labels + offset - 1}. The data type will be the same as label_field, except when offset + number_of_labels causes overflow of the current data type.  
forward_mapArrayMap 

The map from the original label space to the returned label space. Can be used to re-apply the same mapping. See examples for usage. The output data type will be the same as relabeled.  
inverse_mapArrayMap 

The map from the new label space to the original space. This can be used to reconstruct the original label field from the relabeled one. The output data type will be the same as label_field.     Notes The label 0 is assumed to denote the background and is never remapped. The forward map can be extremely big for some inputs, since its length is given by the maximum of the label field. However, in most situations, label_field.max() is much smaller than label_field.size, and in these cases the forward map is guaranteed to be smaller than either the input or output images. Examples >>> from skimage.segmentation import relabel_sequential
>>> label_field = np.array([1, 1, 5, 5, 8, 99, 42])
>>> relab, fw, inv = relabel_sequential(label_field)
>>> relab
array([1, 1, 2, 2, 3, 5, 4])
>>> print(fw)
ArrayMap:
  1 → 1
  5 → 2
  8 → 3
  42 → 4
  99 → 5
>>> np.array(fw)
array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5])
>>> np.array(inv)
array([ 0,  1,  5,  8, 42, 99])
>>> (fw[label_field] == relab).all()
True
>>> (inv[relab] == label_field).all()
True
>>> relab, fw, inv = relabel_sequential(label_field, offset=5)
>>> relab
array([5, 5, 6, 6, 7, 9, 8])
 
 slic  
skimage.segmentation.slic(image, n_segments=100, compactness=10.0, max_iter=10, sigma=0, spacing=None, multichannel=True, convert2lab=None, enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3, slic_zero=False, start_label=None, mask=None) [source]
 
Segments image using k-means clustering in Color-(x,y,z) space.  Parameters 
 
image2D, 3D or 4D ndarray 

Input image, which can be 2D or 3D, and grayscale or multichannel (see multichannel parameter). Input image must either be NaN-free or the NaN’s must be masked out  
n_segmentsint, optional 

The (approximate) number of labels in the segmented output image.  
compactnessfloat, optional 

Balances color proximity and space proximity. Higher values give more weight to space proximity, making superpixel shapes more square/cubic. In SLICO mode, this is the initial compactness. This parameter depends strongly on image contrast and on the shapes of objects in the image. We recommend exploring possible values on a log scale, e.g., 0.01, 0.1, 1, 10, 100, before refining around a chosen value.  
max_iterint, optional 

Maximum number of iterations of k-means.  
sigmafloat or (3,) array-like of floats, optional 

Width of Gaussian smoothing kernel for pre-processing for each dimension of the image. The same sigma is applied to each dimension in case of a scalar value. Zero means no smoothing. Note, that sigma is automatically scaled if it is scalar and a manual voxel spacing is provided (see Notes section).  
spacing(3,) array-like of floats, optional 

The voxel spacing along each image dimension. By default, slic assumes uniform spacing (same voxel resolution along z, y and x). This parameter controls the weights of the distances along z, y, and x during k-means clustering.  
multichannelbool, optional 

Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension.  
convert2labbool, optional 

Whether the input should be converted to Lab colorspace prior to segmentation. The input image must be RGB. Highly recommended. This option defaults to True when multichannel=True and image.shape[-1] == 3.  
enforce_connectivitybool, optional 

Whether the generated segments are connected or not  
min_size_factorfloat, optional 

Proportion of the minimum segment size to be removed with respect to the supposed segment size `depth*width*height/n_segments`  
max_size_factorfloat, optional 

Proportion of the maximum connected segment size. A value of 3 works in most of the cases.  
slic_zerobool, optional 

Run SLIC-zero, the zero-parameter mode of SLIC. [2]  start_label: int, optional

The labels’ index start. Should be 0 or 1.  New in version 0.17: start_label was introduced in 0.17   
mask2D ndarray, optional 

If provided, superpixels are computed only where mask is True, and seed points are homogeneously distributed over the mask using a K-means clustering strategy.  New in version 0.17: mask was introduced in 0.17     Returns 
 
labels2D or 3D array 

Integer mask indicating segment labels.    Raises 
 ValueError

If convert2lab is set to True but the last array dimension is not of length 3.  ValueError

If start_label is not 0 or 1.     Notes  If sigma > 0, the image is smoothed using a Gaussian kernel prior to segmentation. If sigma is scalar and spacing is provided, the kernel width is divided along each dimension by the spacing. For example, if sigma=1 and spacing=[5, 1, 1], the effective sigma is [0.2, 1, 1]. This ensures sensible smoothing for anisotropic images. The image is rescaled to be in [0, 1] prior to processing. Images of shape (M, N, 3) are interpreted as 2D RGB images by default. To interpret them as 3D with the last dimension having length 3, use multichannel=False. 
start_label is introduced to handle the issue [4]. The labels indexing starting at 0 will be deprecated in future versions. If mask is not None labels indexing starts at 1 and masked area is set to 0.  References  
1  
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, TPAMI, May 2012. DOI:10.1109/TPAMI.2012.120  
2  
https://www.epfl.ch/labs/ivrl/research/slic-superpixels/#SLICO  
3  
Irving, Benjamin. “maskSLIC: regional superpixel generation with application to local pathology characterisation in medical images.”, 2016, arXiv:1606.09518  
4  
https://github.com/scikit-image/scikit-image/issues/3722   Examples >>> from skimage.segmentation import slic
>>> from skimage.data import astronaut
>>> img = astronaut()
>>> segments = slic(img, n_segments=100, compactness=10)
 Increasing the compactness parameter yields more square regions: >>> segments = slic(img, n_segments=100, compactness=20)
 
 watershed  
skimage.segmentation.watershed(image, markers=None, connectivity=1, offset=None, mask=None, compactness=0, watershed_line=False) [source]
 
Find watershed basins in image flooded from given markers.  Parameters 
 
imagendarray (2-D, 3-D, …) of integers 

Data array where the lowest value points are labeled first.  
markersint, or ndarray of int, same shape as image, optional 

The desired number of markers, or an array marking the basins with the values to be assigned in the label matrix. Zero means not a marker. If None (no markers given), the local minima of the image are used as markers.  
connectivityndarray, optional 

An array with the same number of dimensions as image whose non-zero elements indicate neighbors for connection. Following the scipy convention, default is a one-connected array of the dimension of the image.  
offsetarray_like of shape image.ndim, optional 

offset of the connectivity (one offset per dimension)  
maskndarray of bools or 0s and 1s, optional 

Array of same shape as image. Only points at which mask == True will be labeled.  
compactnessfloat, optional 

Use compact watershed [3] with given compactness parameter. Higher values result in more regularly-shaped watershed basins.  
watershed_linebool, optional 

If watershed_line is True, a one-pixel wide line separates the regions obtained by the watershed algorithm. The line has the label 0.    Returns 
 
outndarray 

A labeled matrix of the same type and shape as markers      See also  
skimage.segmentation.random_walker


random walker segmentation A segmentation algorithm based on anisotropic diffusion, usually slower than the watershed but with good results on noisy data and boundaries with holes.    Notes This function implements a watershed algorithm [1] [2] that apportions pixels into marked basins. The algorithm uses a priority queue to hold the pixels with the metric for the priority queue being pixel value, then the time of entry into the queue - this settles ties in favor of the closest marker. Some ideas taken from Soille, “Automated Basin Delineation from Digital Elevation Models Using Mathematical Morphology”, Signal Processing 20 (1990) 171-182 The most important insight in the paper is that entry time onto the queue solves two problems: a pixel should be assigned to the neighbor with the largest gradient or, if there is no gradient, pixels on a plateau should be split between markers on opposite sides. This implementation converts all arguments to specific, lowest common denominator types, then passes these to a C algorithm. Markers can be determined manually, or automatically using for example the local minima of the gradient of the image, or the local maxima of the distance function to the background for separating overlapping objects (see example). References  
1  
https://en.wikipedia.org/wiki/Watershed_%28image_processing%29  
2  
http://cmm.ensmp.fr/~beucher/wtshed.html  
3  
Peer Neubert & Peter Protzel (2014). Compact Watershed and Preemptive SLIC: On Improving Trade-offs of Superpixel Segmentation Algorithms. ICPR 2014, pp 996-1001. DOI:10.1109/ICPR.2014.181 https://www.tu-chemnitz.de/etit/proaut/publications/cws_pSLIC_ICPR.pdf   Examples The watershed algorithm is useful to separate overlapping objects. We first generate an initial image with two overlapping circles: >>> x, y = np.indices((80, 80))
>>> x1, y1, x2, y2 = 28, 28, 44, 52
>>> r1, r2 = 16, 20
>>> mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2
>>> mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2
>>> image = np.logical_or(mask_circle1, mask_circle2)
 Next, we want to separate the two circles. We generate markers at the maxima of the distance to the background: >>> from scipy import ndimage as ndi
>>> distance = ndi.distance_transform_edt(image)
>>> from skimage.feature import peak_local_max
>>> local_maxi = peak_local_max(distance, labels=image,
...                             footprint=np.ones((3, 3)),
...                             indices=False)
>>> markers = ndi.label(local_maxi)[0]
 Finally, we run the watershed on the image and markers: >>> labels = watershed(-distance, markers, mask=image)
 The algorithm works also for 3-D images, and can be used for example to separate overlapping spheres. 
 Examples using skimage.segmentation.watershed
 
  Watershed segmentation  

  Markers for watershed transform  

  Segment human cells (in mitosis)