image-cluster.rar_K均值 聚三类_K均值聚_K均值聚类图像_cluster_聚类 图像

he = imread('hestain.png');%读取图像 figure; imshow(he), %显示 title('H&E image'); text(size(he,2),size(he,1)+15,... 'Image courtesy of Alan Partin, Johns Hopkins University', ... 'FontSize',7,'HorizontalAlignment','right'); cform = makecform('srgb2lab');%rgb空间转换成L*a*b*空间的结构 lab_he = applycform(he,cform);%rgb空间转换成L*a*b*空间 ab = double(lab_he(:,:,2:3)); nrows = size(ab,1); ncols = size(ab,2); ab = reshape(ab,nrows*ncols,2); nColors = 3; [cluster_idx cluster_center] = kmeans(ab,nColors,'distance',... 'sqEuclidean', 'Replicates',3);%进行K均值聚类 pixel_labels = reshape(cluster_idx,nrows,ncols); figure; imshow(pixel_labels,[]), %显示聚类分割成的三部分 title('image labeled by cluster index'); segmented_images = cell(1,3); rgb_label = repmat(pixel_labels,[1 1 3]); for k = 1:nColors color = he; color(rgb_label ~= k) = 0;%标号为k外的其他区域置0 segmented_images{k} = color; end figure; subplot(131) imshow(segmented_images{1}), %显示第一个聚类区域 title('objects in cluster 1'); subplot(132), imshow(segmented_images{2}), %显示第二个聚类区域 title('objects in cluster 2'); subplot(133), imshow(segmented_images{3}), %显示第三个聚类区域 title('objects in cluster 3'); mean_cluster_value = mean(cluster_center,2);%计算均值 [tmp, idx] = sort(mean_cluster_value);%排序 blue_cluster_num = idx(1);%最小值 L = lab_he(:,:,1);%亮度分量 blue_idx = find(pixel_labels == blue_cluster_num);%蓝色区域标号 L_blue = L(blue_idx); is_light_blue = im2bw(L_blue,graythresh(L_blue));%转化为二值图像 nuclei_labels = repmat(uint8(0),[nrows ncols]); nuclei_labels(blue_idx(is_light_blue==false)) = 1;%深蓝区域标号 nuclei_labels = repmat(nuclei_labels,[1 1 3]); blue_nuclei = he; blue_nuclei(nuclei_labels ~= 1) = 0;%深蓝区域外其他区域置0 figure, imshow(blue_nuclei), %显示深蓝区域 title('blue nuclei');