separate_stains skimage.color.separate_stains(rgb, conv_matrix) [source] RGB to stain color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3-D array of shape (.., .., 3). conv_matrix: ndarray The stain separation matrix as described by G. Landini [R55]. Returns: out : ndarray The image in stain color space, in a 3-D array of shape (.., .., 3). Raises: ValueError If rgb is not a 3-D array of shape (.., .., 3). Notes Stain separation matrices availab

equalize_adapthist skimage.exposure.equalize_adapthist(image, *args, **kwargs) [source] Contrast Limited Adaptive Histogram Equalization (CLAHE). An algorithm for local contrast enhancement, that uses histograms computed over different tile regions of the image. Local details can therefore be enhanced even in regions that are darker or lighter than most of the image. Parameters: image : array-like Input image. kernel_size: integer or 2-tuple Defines the shape of contextual regions used i

xyz2rgb skimage.color.xyz2rgb(xyz) [source] XYZ to RGB color space conversion. Parameters: xyz : array_like The image in XYZ format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If xyz is not a 3-D array of shape (.., .., 3). Notes The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts to sRGB. References [R60] http://en.wikipedia.org/

hessian_matrix skimage.feature.hessian_matrix(image, sigma=1, mode='constant', cval=0) [source] Compute Hessian matrix. The Hessian matrix is defined as: H = [Hxx Hxy] [Hxy Hyy] which is computed by convolving the image with the second derivatives of the Gaussian kernel in the respective x- and y-directions. Parameters: image : ndarray Input image. sigma : float Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix. mod

threshold_otsu skimage.filters.threshold_otsu(image, nbins=256) [source] Return threshold value based on Otsu’s method. Parameters: image : array Grayscale input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns: threshold : float Upper threshold value. All pixels intensities that less or equal of this value assumed as foreground. Notes The input image must be grayscale. References [R206] Wikipedia, http://en.

moments skimage.measure.moments(image, order=3) [source] Calculate all raw image moments up to a certain order. The following properties can be calculated from raw image moments: Area as: m[0, 0]. Centroid as: {m[0, 1] / m[0, 0], m[1, 0] / m[0, 0]}. Note that raw moments are neither translation, scale nor rotation invariant. Parameters: image : 2D double or uint8 array Rasterized shape as image. order : int, optional Maximum order of moments. Default is 3. Returns: m : (order + 1, o

img_as_uint skimage.util.img_as_uint(image, force_copy=False) [source] Convert an image to 16-bit unsigned integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of uint16 Output image. Notes Negative input values will be clipped. Positive values are scaled between 0 and 65535.

skeletonize_3d skimage.morphology.skeletonize_3d(img) [source] Compute the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters: img : ndarray, 2D or 3D A binary image containing the objects to be skeletonized. Zeros represent background, nonzero values are foreground. Returns: skeleton : ndarray The thinned image. See also skeletonize, medial_axis Notes The method of [Lee94] uses an octree dat

perimeter skimage.measure.perimeter(image, neighbourhood=4) [source] Calculate total perimeter of all objects in binary image. Parameters: image : array Binary image. neighbourhood : 4 or 8, optional Neighborhood connectivity for border pixel determination. Returns: perimeter : float Total perimeter of all objects in binary image. References [R282] K. Benkrid, D. Crookes. Design and FPGA Implementation of a Perimeter Estimator. The Queen’s University of Belfast. http://www.cs.qub.a

star skimage.morphology.star(a, dtype=) [source] Generates a star shaped structuring element. Start has 8 vertices and is an overlap of square of size 2*a + 1 with its 45 degree rotated version. The slanted sides are 45 or 135 degrees to the horizontal axis. Parameters: a : int Parameter deciding the size of the star structural element. The side of the square array returned is 2*a + 1 + 2*floor(a / 2). Returns: selem : ndarray The structuring element where elements of the neighborhood