MATLABR2018b代码，对灰度图像进行OSTU二值化

以下是MATLAB R2018b代码，可用于对灰度图像进行OSTU二值化：

% 读入灰度图像
I = imread('image.jpg');

% 计算灰度图像的直方图
counts = imhist(I);

% 计算灰度图像的像素总数
totalPixels = numel(I);

% 初始化最大方差和最佳阈值
maxVariance = 0;
bestThreshold = 0;

% 遍历灰度级，找到最佳阈值
for threshold = 0:255
    % 计算前景像素数、背景像素数、前景像素平均灰度、背景像素平均灰度
    foregroundPixels = sum(counts(threshold+1:256));
    backgroundPixels = totalPixels - foregroundPixels;
    foregroundMean = sum((threshold+1:256).*counts(threshold+1:256)) / foregroundPixels;
    backgroundMean = sum((0:threshold).*counts(1:threshold+1)) / backgroundPixels;
    
    % 计算类间方差
    variance = foregroundPixels*backgroundPixels*(foregroundMean-backgroundMean)^2;
    
    % 更新最大方差和最佳阈值
    if variance > maxVariance
        maxVariance = variance;
        bestThreshold = threshold;
    end
end

% 对灰度图像进行二值化
binaryImage = imbinarize(I, bestThreshold/255);

% 显示结果
imshow(binaryImage);

请将代码中的`image.jpg`替换为您要处理的图像文件名。