Multilevel image thresholds using Otsu’s method
thresh = multithresh(A)
thresh = multithresh(A,N)
[thresh,metric] = multithresh(___)
Read image and display it.
I = imread('coins.png'); imshow(I)
Calculate a single threshold value for the image.
level = multithresh(I);
Segment the image into two regions using
imquantize , specifying the threshold level returned by
seg_I = imquantize(I,level); figure imshow(seg_I,)
Read image and display it.
I = imread('circlesBrightDark.png'); imshow(I) axis off title('Original Image')
Calculate two threshold levels.
thresh = multithresh(I,2);
Segment the image into three levels using
seg_I = imquantize(I,thresh);
Convert segmented image into color image using
label2rgb and display it.
RGB = label2rgb(seg_I); figure; imshow(RGB) axis off title('RGB Segmented Image')
Read truecolor (RGB) image and display it.
I = imread('peppers.png'); imshow(I) axis off title('RGB Image');
Generate thresholds for seven levels from the entire RGB image.
threshRGB = multithresh(I,7);
Generate thresholds for each plane of the RGB image.
threshForPlanes = zeros(3,7); for i = 1:3 threshForPlanes(i,:) = multithresh(I(:,:,i),7); end
Process the entire image with the set of threshold values computed from entire image.
value = [0 threshRGB(2:end) 255]; quantRGB = imquantize(I, threshRGB, value);
Process each RGB plane separately using the threshold vector computed from the given plane. Quantize each RGB plane using threshold vector generated for that plane.
quantPlane = zeros( size(I) ); for i = 1:3 value = [0 threshForPlanes(i,2:end) 255]; quantPlane(:,:,i) = imquantize(I(:,:,i),threshForPlanes(i,:),value); end quantPlane = uint8(quantPlane);
Display both posterized images and note the visual differences in the two thresholding schemes.
imshowpair(quantRGB,quantPlane,'montage') axis off title('Full RGB Image Quantization Plane-by-Plane Quantization')
To compare the results, calculate the number of unique RGB pixel vectors in each output image. Note that the plane-by-plane thresholding scheme yields about 23% more colors than the full RGB image scheme.
dim = size( quantRGB ); quantRGBmx3 = reshape(quantRGB, prod(dim(1:2)), 3); quantPlanemx3 = reshape(quantPlane, prod(dim(1:2)), 3); colorsRGB = unique(quantRGBmx3, 'rows' ); colorsPlane = unique(quantPlanemx3, 'rows' ); disp(['Unique colors in RGB image : ' int2str(length(colorsRGB))]);
Unique colors in RGB image : 188
disp(['Unique colors in Plane-by-Plane image : ' int2str(length(colorsPlane))]);
Unique colors in Plane-by-Plane image : 231
I = imread('circlesBrightDark.png');
Find all unique grayscale values in image.
uniqLevels = unique(I(:)); disp(['Number of unique levels = ' int2str( length(uniqLevels) )]);
Number of unique levels = 148
Compute a series of thresholds at monotonically increasing values of
Nvals = [1 2 4 8]; for i = 1:length(Nvals) [thresh, metric] = multithresh(I, Nvals(i) ); disp(['N = ' int2str(Nvals(i)) ' | metric = ' num2str(metric)]); end
N = 1 | metric = 0.54767 N = 2 | metric = 0.98715 N = 4 | metric = 0.99648 N = 8 | metric = 0.99902
Apply the set of 8 threshold values to obtain a 9-level segmentation using
seg_Neq8 = imquantize(I,thresh); uniqLevels = unique( seg_Neq8(:) )
uniqLevels = 1 2 3 4 5 6 7 8 9
Threshold the image using
seg_Neq8 as an input to
N equal to 8, which is 1 less than the number of levels in this segmented image.
multithresh returns a
metric value of 1.
[thresh, metric] = multithresh(seg_Neq8,8)
thresh = Columns 1 through 7 1.8784 2.7882 3.6667 4.5451 5.4549 6.3333 7.2118 Column 8 8.1216
metric = 1
Threshold the image again, this time increasing the value of
N by 1. This value now equals the number of levels in the image. Note how the input is degenerate because the number of levels in the image is too few for the number of requested thresholds. Hence, multithresh returns a
metric value of 0.
[thresh, metric] = multithresh(seg_Neq8,9)
Warning: No solution exists because the number of unique levels in the image are too few to find 9 thresholds. Returning an arbitrarily chosen solution.
thresh = 1 2 3 4 5 6 7 8 9
metric = 0
A— Image to be thresholded
Image to be thresholded, specified as a real, nonsparse numeric
array of any dimension.
multithresh finds the
thresholds based on the aggregate histogram of the entire array.
an RGB image as a 3-D numeric array and computes the thresholds for
the combined data from all three color planes.
multithresh uses the range of the input
as the limits for computing the histogram used in subsequent computations.
NaNs in computation. Any
counted in the first and last bin of the histogram, respectively.
For degenerate inputs where the number of unique values in
less than or equal to
N, there is no viable solution
using Otsu's method. For such inputs, the return value
all the unique values from
A and possibly some
extra values that are chosen arbitrarily.
I = imread('cameraman.tif'); thresh
N— Number of threshold values
Number of threshold values, specified as a positive integer
scalar value. For
N > 2,
search-based optimization of Otsu's criterion to find the thresholds.
The search-based optimization guarantees only locally optimal results.
Since the chance of converging to local optimum increases with
it is preferable to use smaller values of
< 10. The maximum allowed value for
thresh = multithresh(I,4);
thresh— Set of threshold values used to quantize an image
metric— Measure of the effectiveness of the thresholds
Measure of the effectiveness of the thresholds, returned as
a scalar value. Higher values indicates greater effectiveness of the
thresholds in separating the input image into
based on Otsu's objective criterion. For degenerate inputs where the
number of unique values in
A is less than or
metric equals 0.
 Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
Usage notes and limitations:
This function supports the generation of C code using MATLAB®
Note that if you choose the generic
MATLAB Host Computer target
platform, the function generates code that uses a precompiled, platform-specific
shared library. Use of a shared library preserves performance optimizations
but limits the target platforms for which code can be generated. For
more information, see Understand Code Generation with Image Processing Toolbox.
The input argument
N must be
a compile-time constant.