returns the value of property "prop" for the entire image.


returns the values in the specified output format.

ImageMeasurements[{image1, image2, },]

returns measurements for all imagei.

Details and Options

  • ImageMeasurements works with arbitrary 2D and 3D images.
  • ImageMeasurements[image,{"prop1","prop2",}] computes multiple properties.
  • ImageMeasurements[image,"Properties"] gives names of all available properties as a list of strings.
  • Position, area, and length measurements are computed in the standard image coordinate system.
  • For images of type "Byte" and "Bit16", ImageMeasurements always normalizes values to lie between 0 and 1.
  • The following properties can be computed on images:
  • Global image properties:
  • "AspectRatio"ratio of height to width
    "Channels"number of image channels
    "ColorSpace"image color space
    "DataRange"range of the underlying data
    "DataType"underlying data type
    "Dimensions"dimensions of the image data
    "ImageDimensions"{width, height} or {width,depth,height} of the image
    "Interleaving"amount of interleaving of the image
    "SampleDepth"number of bits used to represent each pixel
    "Transparency"whether or not the image has an alpha channel
  • Basic histogram properties, measured for each channel separately:
  • "Min"minimum value
    "Max"maximum value
    "MinMax"minimum and maximum values
    "Mean"average value
    "Median"median value
    "StandardDeviation"standard deviation
    "Total"total of all values
  • Basic image intensity properties:
  • "MinIntensity"minimum intensity
    "MaxIntensity"maximum intensity
    "MinMaxIntensity"minimum and maximum intensity
    "MeanIntensity"average intensity
    "MedianIntensity"median intensity
    "StandardDeviationIntensity"standard deviation of the intensity distribution
    "TotalIntensity"total intensity
  • Contour properties:
  • "Contours"lines describing the component boundary
    "ContourHierarchy"topological nesting of the contours
    "PerimeterPositions"sorted positions of the perimeter elements
  • Spatial intensity measurements:
  • "Skew"asymmetry in intensity distribution
    "IntensityCentroid"coordinates of the intensity-weighted centroid
  • Statistical measurements:
  • "Entropy"data entropy (base E)
    "Energy"data energy
  • The following format specifications can be used:
  • Automaticdetermine the output automatically
    "Association"format the result as an Association
    "Dataset"format the result as a Dataset
    "List"format the result as a List
    "RuleList"format the result as a list of Rule expressions
  • ImageMeasurements takes a Masking option. The default setting is Masking->All. The Masking option is ignored when returning global image properties.


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Basic Examples  (2)

Extract mean color value:

Mean pixel intensity:

Global image entropy:

Standard deviation of pixel intensity in a 3D image:

Scope  (9)

Basic Uses  (6)

Test an image to see if it has an alpha channel:

Compute multiple properties of an image:

Compute a property of multiple images:

Compute multiple properties of multiple images:

Get image dimensions:

Compare to "Dimensions" property which gives the data dimensions including channels:

Extract pixel value ranges for each channel:

Output Format  (3)

Format the properties as an Association:

Return a list of rules:

Return a Dataset:

Options  (2)

Masking  (1)

Compute the mean pixel value for a specified region of interest:

CornerNeighbors  (1)

By default, ImageMeasurements assumes 8-connectivity:

Use CornerNeighborsFalse to assume 4-connectivity:

Applications  (5)

Multiply the gradient magnitude of an image by its maximum value, so that the pixels with the largest values are white:

Detect whether an image has constant pixel values:

Ordinal measurement descriptor of an image:

Compute the centroid distance function for the shapes present in an image:

Extract the list of shapes from the image:

Define a function that parametrizes the distance from the contour centroid:

Plot the centroid distance function for some of the shapes:

Define a feature vector sampling the centroid distance:

Use the feature vectors to cluster the shapes:

Compute the Fourier descriptors of a shape:

Extract the contour coordinates and compute the Fourier transform of their complex representation:

The original shape can be reconstructed using only a portion of the descriptors:

Control the contour smoothness by interactively setting the number of descriptors:

Wolfram Research (2012), ImageMeasurements, Wolfram Language function, (updated 2022).


Wolfram Research (2012), ImageMeasurements, Wolfram Language function, (updated 2022).


Wolfram Language. 2012. "ImageMeasurements." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2022.


Wolfram Language. (2012). ImageMeasurements. Wolfram Language & System Documentation Center. Retrieved from


@misc{reference.wolfram_2022_imagemeasurements, author="Wolfram Research", title="{ImageMeasurements}", year="2022", howpublished="\url{}", note=[Accessed: 02-July-2022 ]}


@online{reference.wolfram_2022_imagemeasurements, organization={Wolfram Research}, title={ImageMeasurements}, year={2022}, url={}, note=[Accessed: 02-July-2022 ]}