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regionprops
Measure properties of image regions
Syntax
stats = regionprops(BW,properties)stats = regionprops(CC,properties)stats = regionprops(L,properties)stats = regionprops(___,I,properties)stats = regionprops(output,___)Description
stats = regionprops(BW,properties)properties
for each 8-connected component (object) in the binary image, BW.
stats is struct array containing a struct for each object in the
image. You can use regionprops on contiguous regions and discontiguous
regions (see Algorithms).
Note
To return measurements of a 3-D volumetric image, consider using regionprops3. While regionprops can accept 3-D images,
regionprops3 calculates more statistics for 3-D images than
regionprops.
For all syntaxes, if you do not specify the properties argument, then
regionprops returns the 'Area',
'Centroid', and 'BoundingBox' measurements.
You optionally can measure properties of image regions The 'ConvexArea', 'ConvexHull',
'ConvexImage', 'Circularity',
'EulerNumber', 'FilledArea',
'FilledImage', 'MaxFeretProperties',
'MinFeretProperties' and 'Solidity' properties are
not supported on a GPU.
stats = regionprops(CC,properties)CC, which is a structure returned by bwconncomp.
This syntax is not supported on a GPU.
stats = regionprops(L,properties)L.
stats = regionprops(___,I,properties)properties
for each labeled region in the image I. The first input to
regionprops (BW, CC, or
L) identifies the regions in I.
Examples
Calculate Centroids and Superimpose Locations on Image
Read a binary image into workspace.
BW = imread('text.png');Calculate centroids for connected components in the image using regionprops. The regionprops function returns the centroids in a structure array.
s = regionprops(BW,'centroid');Store the x- and y-coordinates of the centroids into a two-column matrix.
centroids = cat(1,s.Centroid);
Display the binary image with the centroid locations superimposed.
imshow(BW) hold on plot(centroids(:,1),centroids(:,2),'b*') hold off
Calculate Centroids and Superimpose Locations on Image on GPU
Read binary image into a gpuArray.
BW = gpuArray(imread('text.png'));
Calculate the centroids of objects in the image.
s = regionprops(BW,'centroid');
Plot the centroids on the image.
centroids = cat(1, s.Centroid); imshow(BW) hold on plot(centroids(:,1), centroids(:,2), 'b*') hold off
Estimate Center and Radii of Circular Objects and Plot Circles
Estimate the center and radii of circular objects in an image and use this information to plot circles on the image. In this example, regionprops returns the measured region properties in a table.
Read an image into workspace.
a = imread('circlesBrightDark.png');Turn the input image into a binary image.
bw = a < 100;
imshow(bw)
title('Image with Circles')
Calculate properties of regions in the image and return the data in a table.
stats = regionprops('table',bw,'Centroid',... 'MajorAxisLength','MinorAxisLength')
stats=4×3 table
Centroid MajorAxisLength MinorAxisLength
________________ _______________ _______________
256.5 256.5 834.46 834.46
300 120 81.759 81.759
330.47 369.83 111.78 110.36
450 240 101.72 101.72
Get centers and radii of the circles.
centers = stats.Centroid; diameters = mean([stats.MajorAxisLength stats.MinorAxisLength],2); radii = diameters/2;
Plot the circles.
hold on viscircles(centers,radii); hold off
Input Arguments
BW — Binary image
logical array of any dimension | gpuArray
Binary image, specified as a logical array of any dimension.
To measure properties of image regions using a GPU, specify BW
as a gpuArray that contains a 2-D logical
matrix.
Data Types: logical
CC — Connected components
structure
Connected components, specified as a structure returned by bwconncomp.
Data Types: struct
L — Label matrix
numeric array | gpuArray
Label matrix, specified as a numeric array of any dimension.
regionprops treats negative-valued pixels as background and rounds
down input pixels that are not integers. Positive integer elements of
L correspond to different regions. For example, the set of elements
of L equal to 1 corresponds to region 1; the set
of elements of L equal to 2 corresponds to region
2; and so on.
To measure properties of image regions using a GPU, specify L
as a gpuArray that contains a 2-D label
matrix.
Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32
properties — Type of measurement
comma-separated list of string scalars or character vectors | cell array of string scalars or character vectors | 'all' | 'basic'
Type of measurement, specified as a comma-separated list of string scalars or character
vectors, a cell array of string scalars or character vectors, or as
'all' or 'basic'. Property names are
case-insensitive and can be abbreviated. When used with code generation,
regionprops does not support cell arrays of string scalars or
character vectors.
If you specify
'all',regionpropscomputes all the shape measurements and, for grayscale images, the pixel value measurements as well.If you specify
'basic', or do not specify thepropertiesargument,regionpropscomputes only the'Area','Centroid', and'BoundingBox'measurements.
The following tables list all the properties that provide shape measurements. The properties listed in the Pixel Value Measurements table are valid only when you specify a grayscale image.
Shape Measurements
| Property Name | Description | N-D Support | GPU Support | Code Generation | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
'Area' |
Actual number of pixels in the region, returned as a scalar. (This value
might differ slightly from the value returned by To find the equivalent to the area of a 3-D volume, use the
| Yes | Yes | Yes | ||||||||
'BoundingBox' | Smallest rectangle containing the region, returned as a 1-by-Q*2 vector,
where Q is the number of image dimensions. For example, in
the vector [ul_corner width], ul_corner
specifies the upper-left corner of the bounding box in the form [x y z
...]. width specifies the width of the bounding
box along each dimension in the form [x_width y_width ...].
regionprops uses ndims to get the
dimensions of label matrix or binary image, ndims(L), and
numel to get the dimensions of connected components,
numel(CC.ImageSize). | Yes | Yes | Yes | ||||||||
'Centroid' | Center of mass of the region, returned as a 1-by-
| Yes | Yes | Yes | ||||||||
'ConvexArea' | Number of pixels in 'ConvexImage', returned as a scalar. | 2-D only | No | No | ||||||||
'ConvexHull' | Smallest convex polygon that can contain the region, returned as a p-by-2 matrix. Each row of the matrix contains the x- and y-coordinates of one vertex of the polygon. | 2-D only | No | No | ||||||||
'ConvexImage' | Image that specifies the convex hull, with all pixels within the hull filled in (set to
on), returned as a binary image
(logical). The image is the size of the bounding box of the
region. (For pixels that the boundary of the hull passes through,
regionprops uses the same logic as
roipoly to determine whether the pixel is inside or outside
the hull.) | 2-D only | No | No | ||||||||
'Circularity' | Circularity that specifies the roundness of objects, returned as a struct
with field Circularity. The struct contains the circularity
value for each object in the input image. The circularity value is computed as
(4*Area*pi)/(Perimeter2). For
a perfect circle, the circularity value is 1. The input must be a label matrix
or binary image with contiguous regions. If the image contains discontiguous
regions, regionprops returns unexpected results. Circularity is not recommended for very small objects such as a
3*3 square. For such cases the results might exceed the circularity value for
a perfect circle which is 1. | 2-D only | No | Yes | ||||||||
'Eccentricity' | Eccentricity of the ellipse that has the same second-moments as the region, returned as a scalar. The eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 and 1. (0 and 1 are degenerate cases. An ellipse whose eccentricity is 0 is actually a circle, while an ellipse whose eccentricity is 1 is a line segment.) | 2-D only | Yes | Yes | ||||||||
'EquivDiameter' | Diameter of a circle with the same area as the region, returned as a scalar. Computed as
sqrt(4*Area/pi). | 2-D only | Yes | Yes | ||||||||
'EulerNumber' | Number of objects in the region minus the number of holes in those objects, returned as a
scalar. This property is supported only for 2-D label matrices.
regionprops uses 8-connectivity to compute the Euler number
measurement. To learn more about connectivity, see Pixel Connectivity. | 2-D only | No | Yes | ||||||||
'Extent' | Ratio of pixels in the region to pixels in the total bounding box, returned as a scalar.
Computed as the Area divided by the area of the bounding
box. | 2-D only | Yes | Yes | ||||||||
'Extrema' | Extrema points in the region, returned as an 8-by-2 matrix. Each row of the matrix contains
the x- and y-coordinates of one of
the points. The format of the vector is
| 2-D only | Yes | Yes | ||||||||
'FilledArea' | Number of on pixels in FilledImage, returned as a
scalar. | Yes | No | Yes | ||||||||
'FilledImage' | Image the same size as the bounding box of the region, returned as a binary
(
| Yes | No | Yes | ||||||||
'Image' | Image the same size as the bounding box of the region, returned as a binary
(logical) array. The on pixels
correspond to the region, and all other pixels are
off. | Yes | Yes | Yes | ||||||||
'MaxFeretProperties' | Feret properties that include maximum Feret diameter, its relative angle,
and coordinate values, returned as a struct with fields:
| 2-D only | No | No | ||||||||
'MinFeretProperties' | Feret properties that include minimum Feret diameter, its relative angle,
and coordinate values, returned as a struct with fields:
| 2-D only | No | No | ||||||||
'MajorAxisLength' | Length (in pixels) of the major axis of the ellipse that has the same normalized second central moments as the region, returned as a scalar. | 2-D only | Yes | Yes | ||||||||
'MinorAxisLength' | Length (in pixels) of the minor axis of the ellipse that has the same normalized second central moments as the region, returned as a scalar. | 2-D only | Yes | Yes | ||||||||
'Orientation' | Angle between the x-axis and the major axis of the ellipse that has the same second-moments as the region, returned as a scalar. The value is in degrees, ranging from -90 degrees to 90 degrees. This figure illustrates the axes and orientation of the ellipse. The left side of the figure shows an image region and its corresponding ellipse. The right side shows the same ellipse with the solid blue lines representing the axes. The red dots are the foci. The orientation is the angle between the horizontal dotted line and the major axis.
| 2-D only | Yes | Yes | ||||||||
'Perimeter' | Distance around the boundary of the region returned as a scalar.
| 2-D only | Yes | Yes | ||||||||
'PixelIdxList' | Linear indices of the pixels in the region, returned as a p-element vector. | Yes | Yes | Yes | ||||||||
'PixelList' | Locations of pixels in the region, returned as a p-by-Q
matrix. Each row of the matrix has the form [x y z ...] and
specifies the coordinates of one pixel in the region. | Yes | Yes | Yes | ||||||||
'Solidity' | Proportion of the pixels in the convex hull that are also in the region, returned as a
scalar. Computed as Area/ConvexArea. | 2-D only | No | No | ||||||||
'SubarrayIdx' | Elements of L inside the object bounding box, returned as a cell array
that contains indices such that L(idx{:}) extracts the
elements. | Yes | Yes | No |
The pixel value measurement properties in the following table
are valid only when you specify a grayscale image, I.
Pixel Value Measurements
| Property Name | Description | N-D Support | GPU Support | Code Generation |
|---|---|---|---|---|
'MaxIntensity' | Value of the pixel with the greatest intensity in the region, returned as a scalar. | Yes | Yes | Yes |
'MeanIntensity' | Mean of all the intensity values in the region, returned as a scalar. | Yes | Yes | Yes |
'MinIntensity' | Value of the pixel with the lowest intensity in the region, returned as a scalar. | Yes | Yes | Yes |
'PixelValues' | Number of pixels in the region, returned as a p-by-1 vector, where p is the number of pixels in the region. Each element in the vector contains the value of a pixel in the region. | Yes | Yes | Yes |
'WeightedCentroid' | Center of the region based on location and intensity value, returned as a
p-by-Q vector of coordinates. The first
element of WeightedCentroid is the horizontal coordinate (or
x-coordinate) of the weighted centroid. The second
element is the vertical coordinate (or y-coordinate). All
other elements of WeightedCentroid are in order of dimension. | Yes | Yes | Yes |
Data Types: char | string | cell
output — Return type
'struct' (default) | 'table'
Return type, specified as either of the following values.
| Value | Description |
|---|---|
'struct' | Returns an array of structures with length equal to the number of objects in
BW,
max(. The fields of the
structure array denote different properties for each region, as specified by
properties. |
'table' | Returns a MATLAB® table with height (number of rows) equal to the number of
objects in Not supported on a GPU. |
Data Types: char | string
Output Arguments
Tips
The function
ismemberis useful withregionprops,bwconncomp, andlabelmatrixfor creating a binary image containing only objects or regions that meet certain criteria. For example, these commands create a binary image containing only the regions whose area is greater than 80 and whose eccentricity is less than 0.8.cc = bwconncomp(BW); stats = regionprops(cc, 'Area','Eccentricity'); idx = find([stats.Area] > 80 & [stats.Eccentricity] < 0.8); BW2 = ismember(labelmatrix(cc), idx);
The comma-separated list syntax for structure arrays is useful when you work with the output of
regionprops. For a field that contains a scalar, you can use this syntax to create a vector containing the value of this field for each region in the image. For instance, ifstatsis a structure array with fieldArea, then the following expression:stats(1).Area, stats(2).Area, ..., stats(end).Area
is equivalent to:
stats.Area
Therefore, you can use these calls to create a vector containing the area of each region in the image.
allAreais a vector of the same length as the structure arraystats.stats = regionprops(L, 'Area'); allArea = [stats.Area];
The functions
bwlabel,bwlabeln, andbwconncompall compute connected components for binary images.bwconncompreplaces the use ofbwlabelandbwlabeln. It uses less memory and is sometimes faster than the other functions.Function Input Dimension Output Form Memory Use Connectivity bwlabel2-D Label matrix with double-precision High 4 or 8 bwlabelnN-D Double-precision label matrix High Any bwconncompN-D CCstructLow Any The output of
bwlabelandbwlabelnis a double-precision label matrix. To compute a label matrix using a more memory-efficient data type, use thelabelmatrixfunction on the output ofbwconncomp:CC = bwconncomp(BW); L = labelmatrix(CC);
If you are measuring components in a binary image with default connectivity, it is no longer necessary to call
bwlabelorbwlabelnfirst. You can pass the binary image directly toregionprops, which then uses the memory-efficientbwconncompfunction to compute the connected components automatically. To specify nondefault connectivity, callbwconncompand pass the result toregionprops.CC = bwconncomp(BW, CONN); S = regionprops(CC);
Most of the measurements take little time to compute. However, the following measurements can take longer, depending on the number of regions in
L:'ConvexHull''ConvexImage''ConvexArea''FilledImage'
Computing certain groups of measurements takes about the same amount of time as computing just one of them.
regionpropstakes advantage of intermediate computations useful to each computation. Therefore, it is fastest to compute all the desired measurements in a single call toregionprops.
Algorithms
Contiguous regions are also called objects, connected components, or blobs. A label matrix containing contiguous regions might look like this:
1 1 0 2 2 0 3 3 1 1 0 2 2 0 3 3
L equal
to 1 belong to the first contiguous region or connected component;
elements of L equal to 2 belong to the second connected
component; and so on.Discontiguous regions are regions that might contain multiple connected components. A label matrix containing discontiguous regions might look like this:
1 1 0 1 1 0 2 2 1 1 0 1 1 0 2 2
L equal
to 1 belong to the first region, which is discontiguous and contains
two connected components. Elements of L equal to
2 belong to the second region, which is a single connected component. Extended Capabilities
See Also
bwconncomp | bwferet | bwlabel | bwlabeln | bwpropfilt | ismember | labelmatrix | regionprops3 | watershed
