otsuthresh

Global histogram threshold using Otsu's method

Syntax

T = otsuthresh(counts)

[T,EM] = otsuthresh(counts)

Description

T = otsuthresh(counts) computes a global threshold T from histogram counts, counts, using Otsu's method [1]. Otsu's method chooses a threshold that minimizes the intraclass variance of the thresholded black and white pixels. The global threshold T can be used with imbinarize to convert a grayscale image to a binary image.

[T,EM] = otsuthresh(counts) returns the effectiveness metric, EM, which indicates the effectiveness of the thresholding.

Examples

Compute Threshold from Image Histogram and Binarize Image

Read image into the workspace.

I = imread('coins.png');

Calculate a 16-bin histogram for the image.

[counts,x] = imhist(I,16);
stem(x,counts)

Compute a global threshold using the histogram counts.

T = otsuthresh(counts);

Create a binary image using the computed threshold and display the image.

BW = imbinarize(I,T);
figure
imshow(BW)

Input Arguments

counts — Histogram counts

vector of nonnegative numbers

Histogram counts, specified as a vector of nonnegative numbers.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Output Arguments

T — Global threshold

numeric scalar

Global threshold, returned as a numeric scalar in the range [0, 1].

Data Types: double

EM — Effectiveness metric

numeric scalar

Effectiveness metric of the threshold, returned as a numeric scalar in the range [0, 1]. The lower bound is attainable only by histogram counts with all data in a single non-zero bin. The upper bound is attainable only by histogram counts with two non-zero bins.

Data Types: double

References

[1] 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.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

otsuthresh supports the generation of C code (requires MATLAB^® Coder™). For more information, see Code Generation for Image Processing.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

Version History

Introduced in R2016a

R2021b: Support for thread-based environments

otsuthresh now supports thread-based environments.