Module: filters.rank

skimage.filters.rank.autolevel(image, selem)	Auto-level image using local histogram.
skimage.filters.rank.autolevel_percentile(…)	Return greyscale local autolevel of an image.
skimage.filters.rank.bottomhat(image, selem)	Local bottom-hat of an image.
skimage.filters.rank.enhance_contrast(image, …)	Enhance contrast of an image.
skimage.filters.rank.enhance_contrast_percentile(…)	Enhance contrast of an image.
skimage.filters.rank.entropy(image, selem[, …])	Local entropy.
skimage.filters.rank.equalize(image, selem)	Equalize image using local histogram.
skimage.filters.rank.geometric_mean(image, selem)	Return local geometric mean of an image.
skimage.filters.rank.gradient(image, selem)	Return local gradient of an image (i.e.
skimage.filters.rank.gradient_percentile(…)	Return local gradient of an image (i.e.
skimage.filters.rank.majority(image, selem, *)	Majority filter assign to each pixel the most occuring value within its neighborhood.
skimage.filters.rank.maximum(image, selem[, …])	Return local maximum of an image.
skimage.filters.rank.mean(image, selem[, …])	Return local mean of an image.
skimage.filters.rank.mean_bilateral(image, selem)	Apply a flat kernel bilateral filter.
skimage.filters.rank.mean_percentile(image, …)	Return local mean of an image.
skimage.filters.rank.median(image[, selem, …])	Return local median of an image.
skimage.filters.rank.minimum(image, selem[, …])	Return local minimum of an image.
skimage.filters.rank.modal(image, selem[, …])	Return local mode of an image.
skimage.filters.rank.noise_filter(image, selem)	Noise feature.
skimage.filters.rank.otsu(image, selem[, …])	Local Otsu’s threshold value for each pixel.
skimage.filters.rank.percentile(image, selem)	Return local percentile of an image.
skimage.filters.rank.pop(image, selem[, …])	Return the local number (population) of pixels.
skimage.filters.rank.pop_bilateral(image, selem)	Return the local number (population) of pixels.
skimage.filters.rank.pop_percentile(image, selem)	Return the local number (population) of pixels.
skimage.filters.rank.subtract_mean(image, selem)	Return image subtracted from its local mean.
skimage.filters.rank.subtract_mean_percentile(…)	Return image subtracted from its local mean.
skimage.filters.rank.sum(image, selem[, …])	Return the local sum of pixels.
skimage.filters.rank.sum_bilateral(image, selem)	Apply a flat kernel bilateral filter.
skimage.filters.rank.sum_percentile(image, selem)	Return the local sum of pixels.
skimage.filters.rank.threshold(image, selem)	Local threshold of an image.
skimage.filters.rank.threshold_percentile(…)	Local threshold of an image.
skimage.filters.rank.tophat(image, selem[, …])	Local top-hat of an image.
skimage.filters.rank.windowed_histogram(…)	Normalized sliding window histogram

autolevel

skimage.filters.rank.autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Auto-level image using local histogram.

This filter locally stretches the histogram of gray values to cover the entire range of values from “white” to “black”.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import autolevel
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> auto = autolevel(img, disk(5))
>>> auto_vol = autolevel(volume, ball(5))

Examples using skimage.filters.rank.autolevel

autolevel_percentile

skimage.filters.rank.autolevel_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source]

Return greyscale local autolevel of an image.

This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”.

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

Examples using skimage.filters.rank.autolevel_percentile

bottomhat

skimage.filters.rank.bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source]

Local bottom-hat of an image.

This filter computes the morphological closing of the image and then subtracts the result from the original image.

Parameters

image2-D array (integer or float)

Input image.

selem2-D array (integer or float)

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (integer or float), optional

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint, optional

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out2-D array (same dtype as input image)

Output image.

Warns

Deprecated:

New in version 0.17.

This function is deprecated and will be removed in scikit-image 0.19. This filter was misnamed and we believe that the usefulness is narrow.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk
>>> from skimage.filters.rank import bottomhat
>>> img = data.camera()
>>> out = bottomhat(img, disk(5))  

enhance_contrast

skimage.filters.rank.enhance_contrast(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Enhance contrast of an image.

This replaces each pixel by the local maximum if the pixel gray value is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import enhance_contrast
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> out = enhance_contrast(img, disk(5))
>>> out_vol = enhance_contrast(volume, ball(5))

Examples using skimage.filters.rank.enhance_contrast

enhance_contrast_percentile

skimage.filters.rank.enhance_contrast_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source]

Enhance contrast of an image.

This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum.

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

Examples using skimage.filters.rank.enhance_contrast_percentile

entropy

skimage.filters.rank.entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Local entropy.

The entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode the local gray level distribution.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (float)

Output image.

References

1

https://en.wikipedia.org/wiki/Entropy_(information_theory)

Examples

>>> from skimage import data
>>> from skimage.filters.rank import entropy
>>> from skimage.morphology import disk, ball
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> ent = entropy(img, disk(5))
>>> ent_vol = entropy(volume, ball(5))

Examples using skimage.filters.rank.entropy

equalize

skimage.filters.rank.equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Equalize image using local histogram.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import equalize
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> equ = equalize(img, disk(5))
>>> equ_vol = equalize(volume, ball(5))

Examples using skimage.filters.rank.equalize

geometric_mean

skimage.filters.rank.geometric_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return local geometric mean of an image.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

References

1

Gonzalez, R. C. and Wood, R. E. “Digital Image Processing (3rd Edition).” Prentice-Hall Inc, 2006.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import mean
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> avg = geometric_mean(img, disk(5))
>>> avg_vol = geometric_mean(volume, ball(5))

gradient

skimage.filters.rank.gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return local gradient of an image (i.e. local maximum - local minimum).

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import gradient
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> out = gradient(img, disk(5))
>>> out_vol = gradient(volume, ball(5))

Examples using skimage.filters.rank.gradient

gradient_percentile

skimage.filters.rank.gradient_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source]

Return local gradient of an image (i.e. local maximum - local minimum).

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

majority

skimage.filters.rank.majority(image, selem, *, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Majority filter assign to each pixel the most occuring value within its neighborhood.

Parameters

imagendarray

Image array (uint8, uint16 array).

selem2-D array (integer or float)

The neighborhood expressed as a 2-D array of 1’s and 0’s.

outndarray (integer or float), optional

If None, a new array will be allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint, optional

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out2-D array (same dtype as input image)

Output image.

Examples

>>> from skimage import data
>>> from skimage.filters.rank import majority
>>> from skimage.morphology import disk, ball
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> maj_img = majority(img, disk(5))
>>> maj_img_vol = majority(volume, ball(5))

maximum

skimage.filters.rank.maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return local maximum of an image.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

See also

skimage.morphology.dilation

Notes

The lower algorithm complexity makes skimage.filters.rank.maximum more efficient for larger images and structuring elements.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import maximum
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> out = maximum(img, disk(5))
>>> out_vol = maximum(volume, ball(5))

Examples using skimage.filters.rank.maximum

mean

skimage.filters.rank.mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return local mean of an image.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import mean
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> avg = mean(img, disk(5))
>>> avg_vol = mean(volume, ball(5))

Examples using skimage.filters.rank.mean

mean_bilateral

skimage.filters.rank.mean_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source]

Apply a flat kernel bilateral filter.

This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity.

Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element.

Radiometric similarity is defined by the greylevel interval [g-s0, g+s1] where g is the current pixel greylevel.

Only pixels belonging to the structuring element and having a greylevel inside this interval are averaged.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

s0, s1int

Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

See also

denoise_bilateral

Examples

>>> from skimage import data
>>> from skimage.morphology import disk
>>> from skimage.filters.rank import mean_bilateral
>>> img = data.camera().astype(np.uint16)
>>> bilat_img = mean_bilateral(img, disk(20), s0=10,s1=10)

Examples using skimage.filters.rank.mean_bilateral

mean_percentile

skimage.filters.rank.mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source]

Return local mean of an image.

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

median

skimage.filters.rank.median(image, selem=None, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return local median of an image.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s. If None, a full square of size 3 is used.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

See also

skimage.filters.median

Implementation of a median filtering which handles images with floating precision.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import median
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> med = median(img, disk(5))
>>> med_vol = median(volume, ball(5))

Examples using skimage.filters.rank.median

minimum

skimage.filters.rank.minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return local minimum of an image.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

See also

skimage.morphology.erosion

Notes

The lower algorithm complexity makes skimage.filters.rank.minimum more efficient for larger images and structuring elements.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import minimum
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> out = minimum(img, disk(5))
>>> out_vol = minimum(volume, ball(5))

Examples using skimage.filters.rank.minimum

modal

skimage.filters.rank.modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return local mode of an image.

The mode is the value that appears most often in the local histogram.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import modal
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> out = modal(img, disk(5))
>>> out_vol = modal(volume, ball(5))

noise_filter

skimage.filters.rank.noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Noise feature.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

References

1

N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import noise_filter
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> out = noise_filter(img, disk(5))
>>> out_vol = noise_filter(volume, ball(5))

otsu

skimage.filters.rank.otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Local Otsu’s threshold value for each pixel.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

References

1

https://en.wikipedia.org/wiki/Otsu’s_method

Examples

>>> from skimage import data
>>> from skimage.filters.rank import otsu
>>> from skimage.morphology import disk, ball
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> local_otsu = otsu(img, disk(5))
>>> thresh_image = img >= local_otsu
>>> local_otsu_vol = otsu(volume, ball(5))
>>> thresh_image_vol = volume >= local_otsu_vol

Examples using skimage.filters.rank.otsu

percentile

skimage.filters.rank.percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0) [source]

Return local percentile of an image.

Returns the value of the p0 lower percentile of the local greyvalue distribution.

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0float in [0, …, 1]

Set the percentile value.

Returns

out2-D array (same dtype as input image)

Output image.

pop

skimage.filters.rank.pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return the local number (population) of pixels.

The number of pixels is defined as the number of pixels which are included in the structuring element and the mask.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Examples

>>> from skimage.morphology import square, cube # Need to add 3D example
>>> import skimage.filters.rank as rank
>>> img = 255 * np.array([[0, 0, 0, 0, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> rank.pop(img, square(3))
array([[4, 6, 6, 6, 4],
       [6, 9, 9, 9, 6],
       [6, 9, 9, 9, 6],
       [6, 9, 9, 9, 6],
       [4, 6, 6, 6, 4]], dtype=uint8)

pop_bilateral

skimage.filters.rank.pop_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source]

Return the local number (population) of pixels.

The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Additionally pixels must have a greylevel inside the interval [g-s0, g+s1] where g is the greyvalue of the center pixel.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

s0, s1int

Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

Examples

>>> from skimage.morphology import square
>>> import skimage.filters.rank as rank
>>> img = 255 * np.array([[0, 0, 0, 0, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> rank.pop_bilateral(img, square(3), s0=10, s1=10)
array([[3, 4, 3, 4, 3],
       [4, 4, 6, 4, 4],
       [3, 6, 9, 6, 3],
       [4, 4, 6, 4, 4],
       [3, 4, 3, 4, 3]], dtype=uint16)

pop_percentile

skimage.filters.rank.pop_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source]

Return the local number (population) of pixels.

The number of pixels is defined as the number of pixels which are included in the structuring element and the mask.

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

subtract_mean

skimage.filters.rank.subtract_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return image subtracted from its local mean.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Notes

Subtracting the mean value may introduce underflow. To compensate this potential underflow, the obtained difference is downscaled by a factor of 2 and shifted by n_bins / 2 - 1, the median value of the local histogram (n_bins = max(3, image.max()) +1 for 16-bits images and 256 otherwise).

Examples

>>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import subtract_mean
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> out = subtract_mean(img, disk(5))
>>> out_vol = subtract_mean(volume, ball(5))

subtract_mean_percentile

skimage.filters.rank.subtract_mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source]

Return image subtracted from its local mean.

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

sum

skimage.filters.rank.sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Return the local sum of pixels.

Note that the sum may overflow depending on the data type of the input array.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Examples

>>> from skimage.morphology import square, cube # Need to add 3D example
>>> import skimage.filters.rank as rank         # Cube seems to fail but
>>> img = np.array([[0, 0, 0, 0, 0],            # Ball can pass
...                 [0, 1, 1, 1, 0],
...                 [0, 1, 1, 1, 0],
...                 [0, 1, 1, 1, 0],
...                 [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> rank.sum(img, square(3))
array([[1, 2, 3, 2, 1],
       [2, 4, 6, 4, 2],
       [3, 6, 9, 6, 3],
       [2, 4, 6, 4, 2],
       [1, 2, 3, 2, 1]], dtype=uint8)

sum_bilateral

skimage.filters.rank.sum_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source]

Apply a flat kernel bilateral filter.

This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity.

Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element (selem).

Radiometric similarity is defined by the greylevel interval [g-s0, g+s1] where g is the current pixel greylevel.

Only pixels belonging to the structuring element AND having a greylevel inside this interval are summed.

Note that the sum may overflow depending on the data type of the input array.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

s0, s1int

Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

See also

denoise_bilateral

Examples

>>> from skimage import data
>>> from skimage.morphology import disk
>>> from skimage.filters.rank import sum_bilateral
>>> img = data.camera().astype(np.uint16)
>>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10)

sum_percentile

skimage.filters.rank.sum_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source]

Return the local sum of pixels.

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Note that the sum may overflow depending on the data type of the input array.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.

Returns

out2-D array (same dtype as input image)

Output image.

threshold

skimage.filters.rank.threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False, shift_z=False) [source]

Local threshold of an image.

The resulting binary mask is True if the gray value of the center pixel is greater than the local mean.

Parameters

image([P,] M, N) ndarray (uint8, uint16)

Input image.

selemndarray

The neighborhood expressed as an ndarray of 1’s and 0’s.

out([P,] M, N) array (same dtype as input)

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y, shift_zint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out([P,] M, N) ndarray (same dtype as input image)

Output image.

Examples

>>> from skimage.morphology import square, cube # Need to add 3D example
>>> from skimage.filters.rank import threshold
>>> img = 255 * np.array([[0, 0, 0, 0, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> threshold(img, square(3))
array([[0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 0, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)

threshold_percentile

skimage.filters.rank.threshold_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0) [source]

Local threshold of an image.

The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean.

Only greyvalues between percentiles [p0, p1] are considered in the filter.

Parameters

image2-D array (uint8, uint16)

Input image.

selem2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (same dtype as input)

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0float in [0, …, 1]

Set the percentile value.

Returns

out2-D array (same dtype as input image)

Output image.

tophat

skimage.filters.rank.tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source]

Local top-hat of an image.

This filter computes the morphological opening of the image and then subtracts the result from the original image.

Parameters

image2-D array (integer or float)

Input image.

selem2-D array (integer or float)

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (integer or float), optional

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint, optional

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out2-D array (same dtype as input image)

Output image.

Warns

Deprecated:

New in version 0.17.

This function is deprecated and will be removed in scikit-image 0.19. This filter was misnamed and we believe that the usefulness is narrow.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk
>>> from skimage.filters.rank import tophat
>>> img = data.camera()
>>> out = tophat(img, disk(5))  

windowed_histogram

skimage.filters.rank.windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None) [source]

Normalized sliding window histogram

Parameters

image2-D array (integer or float)

Input image.

selem2-D array (integer or float)

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array (integer or float), optional

If None, a new array is allocated.

maskndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint, optional

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

n_binsint or None

The number of histogram bins. Will default to image.max() + 1 if None is passed.

Returns

out3-D array (float)

Array of dimensions (H,W,N), where (H,W) are the dimensions of the input image and N is n_bins or image.max() + 1 if no value is provided as a parameter. Effectively, each pixel is a N-D feature vector that is the histogram. The sum of the elements in the feature vector will be 1, unless no pixels in the window were covered by both selem and mask, in which case all elements will be 0.

Examples

>>> from skimage import data
>>> from skimage.filters.rank import windowed_histogram
>>> from skimage.morphology import disk, ball
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> hist_img = windowed_histogram(img, disk(5))