MATLAB 图像处理：条纹长度统计及分布分析

% 读取图像 img = imread('7-1.tif');

% 将图像转为二值化矩阵 bw = im2bw(img);

% 骨架化操作 skel = bwmorph(bw, 'skel', Inf);

% 消除分支条纹 skel_pruned = bwmorph(skel, 'spur', 2);

% 计算条纹长度 pixel_size = 0.0595; % 空间分辨率为0.0595纳米/像素 line_lengths = []; for i = 2:size(skel_pruned, 1)-1 for j = 2:size(skel_pruned, 2)-1 if skel_pruned(i, j) == 1 % 判断是否为对角线像素 if mod(i+j,2) == 0 pixel_length = pixel_size * 2; else pixel_length = pixel_size; end % 计算条纹长度 [connectivity, num] = bwlabel(skel_pruned, 8); pixel_count = sum(connectivity(:) == connectivity(i, j)); line_length = pixel_count * pixel_length; % 消除短条纹 if line_length >= 0.483 && i <= size(skel_pruned, 1)-2 && j <= size(skel_pruned, 2)-2 % 修改2：限制 i 和 j 的取值范围，避免越界 line_lengths = [line_lengths, line_length]; end end end end

% 统计数据和图像展示 disp(['平均长度：', num2str(mean(line_lengths)), '纳米']); disp(['最长长度：', num2str(max(line_lengths)), '纳米']); disp(['最短长度：', num2str(min(line_lengths)), '纳米']); disp(['条纹数量：', num2str(length(line_lengths))]);

histogram(line_lengths); xlabel('条纹长度（纳米）'); ylabel('数量'); title('条纹长度分布图');