Module: segmentation

skimage.segmentation.active_contour(image, snake) Active contour model.
skimage.segmentation.clear_border(labels[, ...]) Clear objects connected to the label image border.
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.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.quickshift 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_from_one(*args, ...) Deprecated function. Use relabel_sequential instead.
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.

active_contour

skimage.segmentation.active_contour(image, snake, alpha=0.01, beta=0.1, w_line=0, w_edge=1, gamma=0.01, bc='periodic', max_px_move=1.0, max_iterations=2500, convergence=0.1)[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.

Parameters:

image : (N, M) or (N, M, 3) ndarray

Input image.

snake : (N, 2) ndarray

Initialisation coordinates of snake. For periodic snakes, it should not include duplicate endpoints.

alpha : float, optional

Snake length shape parameter. Higher values makes snake contract faster.

beta : float, optional

Snake smoothness shape parameter. Higher values makes snake smoother.

w_line : float, optional

Controls attraction to brightness. Use negative values to attract to dark regions.

w_edge : float, optional

Controls attraction to edges. Use negative values to repel snake from edges.

gamma : float, optional

Explicit time stepping parameter.

bc : {‘periodic’, ‘free’, ‘fixed’}, optional

Boundary conditions for worm. ‘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.

max_px_move : float, optional

Maximum pixel distance to move per iteration.

max_iterations : int, optional

Maximum iterations to optimize snake shape.

convergence: float, optional

Convergence criteria.
Returns:

snake : (N, 2) ndarray

Optimised snake, same shape as input parameter.

References

[R349]Kass, M.; Witkin, A.; Terzopoulos, D. “Snakes: Active contour models”. International Journal of Computer Vision 1 (4): 321 (1988).

Examples

>>> from skimage.draw import circle_perimeter
>>> from skimage.filters import gaussian_filter

Create and smooth image:

>>> img = np.zeros((100, 100))
>>> rr, cc = circle_perimeter(35, 45, 25)
>>> img[rr, cc] = 1
>>> img = gaussian_filter(img, 2)

Initiliaze spline:

>>> s = np.linspace(0, 2*np.pi,100)
>>> init = 50*np.array([np.cos(s), np.sin(s)]).T+50

Fit spline to image:

>>> snake = active_contour(img, init, w_edge=0, w_line=1) 
>>> dist = np.sqrt((45-snake[:, 0])**2 +(35-snake[:, 1])**2) 
>>> int(np.mean(dist)) 
25

clear_border

skimage.segmentation.clear_border(labels, buffer_size=0, bgval=0, in_place=False)[source]

Clear objects connected to the label image border.

The changes will be applied directly to the input.

Parameters:

labels : (N, M) array of int

Label or binary image.

buffer_size : int, optional

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

bgval : float or int, optional

Cleared objects are set to this value.

in_place : bool, optional

Whether or not to manipulate the labels array in-place.
Returns:

labels : (N, M) array

Cleared binary image.

Examples

>>> import numpy as np
>>> from skimage.segmentation import clear_border
>>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
...                    [0, 0, 0, 0, 1, 0, 0, 0, 0],
...                    [1, 0, 0, 1, 0, 1, 0, 0, 0],
...                    [0, 0, 1, 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, 1, 1, 1, 1, 1, 0, 0],
       [0, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0]])

felzenszwalb

skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20)[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 computes a separate segmentation for each channel and then combines these. The combined segmentation is the intersection of the separate segmentations on the color channels.

Parameters:

image : (width, height, 3) or (width, height) ndarray

Input image.

scale : float

Free parameter. Higher means larger clusters.

sigma : float

Width of Gaussian kernel used in preprocessing.

min_size : int

Minimum component size. Enforced using postprocessing.
Returns:

segment_mask : (width, height) ndarray

Integer mask indicating segment labels.

References

[R350]Efficient graph-based image segmentation, Felzenszwalb, P.F. and Huttenlocher, D.P. International Journal of Computer Vision, 2004

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_img : array of int or bool

An array in which different regions are labeled with either different integers or boolean values.

connectivity: int 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.

mode: string 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.

background: int, optional

For modes ‘inner’ and ‘outer’, a definition of a background label is required. See mode for descriptions of these two.
Returns:

boundaries : array 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=np.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]], dtype=bool)

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, s2 : numpy arrays

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

j : numpy 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.

color : length-3 sequence, optional

RGB color of boundaries in the output image.

outline_color : length-3 sequence, optional

RGB color surrounding boundaries in the output image. If None, no outline is drawn.

mode : string in {‘thick’, ‘inner’, ‘outer’, ‘subpixel’}, optional

The mode for finding boundaries.

background_label : int, 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

quickshift

skimage.segmentation.quickshift()

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.

ratio : float, optional, between 0 and 1 (default 1).

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

kernel_size : float, optional (default 5)

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

max_dist : float, optional (default 10)

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

return_tree : bool, optional (default False)

Whether to return the full segmentation hierarchy tree and distances.

sigma : float, optional (default 0)

Width for Gaussian smoothing as preprocessing. Zero means no smoothing.

convert2lab : bool, optional (default True)

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

random_seed : None (default) or int, 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

[R351]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='bf', tol=0.001, copy=True, multichannel=False, return_full_prob=False, spacing=None)[source]

Random walker algorithm for segmentation from markers.

Random walker algorithm is implemented for gray-level or multichannel images.

Parameters:

data : array_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.

labels : array 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.

beta : float

Penalization coefficient for the random walker motion (the greater beta, the more difficult the diffusion).

mode : string, available options {‘cg_mg’, ‘cg’, ‘bf’}

Mode for solving the linear system in the random walker algorithm. If no preference given, automatically attempt to use the fastest option available (‘cg_mg’ from pyamg >> ‘cg’ with UMFPACK > ‘bf’).

  • ‘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_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 (http://pyamg.org/) is installed. For images of size > 512x512, this is the recommended (fastest) mode.

tol : float

tolerance to achieve when solving the linear system, in cg’ and ‘cg_mg’ modes.

copy : bool

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.

multichannel : bool, default False

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

return_full_prob : bool, default False

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

spacing : iterable of floats

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

output : ndarray

  • If return_full_prob is False, array of ints of same shape as data, 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, data.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 Random walks for image segmentation, Leo Grady, IEEE Trans Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83.

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.

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)
>>> 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_from_one

skimage.segmentation.relabel_from_one(*args, **kwargs)[source]

Deprecated function. Use relabel_sequential instead.

Convert labels in an arbitrary label field to {1, ... number_of_labels}.

This function is deprecated, see relabel_sequential for more.

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_field : numpy array of int, arbitrary shape

An array of labels.

offset : int, optional

The return labels will start at offset, which should be strictly positive.
Returns:

relabeled : numpy array of int, same shape as label_field

The input label field with labels mapped to {offset, ..., number_of_labels + offset - 1}.

forward_map : numpy array of int, shape (label_field.max() + 1,)

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.

inverse_map : 1D numpy array of int, of length offset + number of labels

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.

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])
>>> 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])
>>> 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)[source]

Segments image using k-means clustering in Color-(x,y,z) space.

Parameters:

image : 2D, 3D or 4D ndarray

Input image, which can be 2D or 3D, and grayscale or multichannel (see multichannel parameter).

n_segments : int, optional

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

compactness : float, 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_iter : int, optional

Maximum number of iterations of k-means.

sigma : float 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.

multichannel : bool, optional

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

convert2lab : bool, 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_connectivity: bool, optional

Whether the generated segments are connected or not

min_size_factor: float, optional

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

max_size_factor: float, optional

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

slic_zero: bool, optional

Run SLIC-zero, the zero-parameter mode of SLIC. [R353]
Returns:

labels : 2D 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.

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.

References

[R352]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.
[R353](1, 2) http://ivrg.epfl.ch/research/superpixels#SLICO

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)

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