Question

我正在进行图像分割，测试许多不同的分割算法，以便进行比较研究。目前我已经实现了k-means算法。我想自动计算图像中分割的对象（每个图像上的粉红色和绿色）。我有专家手动计数，所以我想比较结果。

我的输入图片是： enter image description here

我的结果图片是：

enter image description here

我有什么方法可以自动化这个过程吗？

你能帮帮我吗？

提前多多感谢。约翰

Answer 1

好的，这个方法很大程度上基于发布的link ImNt和matlabs label2rgb示例。我不确定你如何聚集区域，但是你从聚类中获得的任何掩码/结果应该用作我的代码的输入

这两个例子中缺少的部分是计算集群。获取簇数的关键是使用label函数的输出。标签功能将所有连接的像素组标记为1到NUMBER_OF_LABELS，因此使用max(labels(:))与计算我们所有的簇相同

我处理了两个完全相同的集群，通常我不会保留所有中间变量，它们只是在这里，所以你可以在每次操作后看到输出

基本大纲

获取二元掩码（您可以自定义）

填写任何漏洞

删除小区域（您可以自定义）

标记所有区域（这基本上是连接组件分组）

计算区域

matlab代码

%% creates binary masks for each cluster
cluster1 = imread('cluster1.png');
cluster1 = rgb2gray(cluster1);

cluster2 = imread('cluster2.png');
cluster2 = rgb2gray(cluster2);

%im not sure how you found these clusters 1/2, but using that original mask is
%better than using this 'graythresh' Since I didn't have your original
%clustering output I tried to reconstruct it w
bw1 = im2bw(cluster1,graythresh(cluster1));
bw2 = im2bw(cluster2,graythresh(cluster2));

%alternate method for constructing binary images   
%my_gray_thresh = 0
%bw1 = cluster1 > my_gray_thresh;
%bw2 = cluster2 > my_gray_thresh;

%% the above could be replaced with the output of your clustering alg

%tries to clean up the image a bit, filling in holes
fill_bw1 = imfill(bw1,'holes');
fill_bw2 = imfill(bw2,'holes');

%takes out regions with less than smallest_area_for_dots
smallest_area_for_dots = 5; %you can play with this parameter
large_bw1 = bwareaopen(fill_bw1, smallest_area_for_dots);
large_bw2 = bwareaopen(fill_bw2, smallest_area_for_dots);

%labels the regions, this is essentially connected component analysis 
labels1 = bwlabel(large_bw1);
labels2 = bwlabel(large_bw2);

%plots the labeled image
figure(1)
subplot(2,3,[1 4]);imshow(cluster1);title('original image (grayscale)')
subplot(2,3,2);imshow(bw1);title('bw after threshold')
subplot(2,3,3);imshow(fill_bw1);title('after fill')
subplot(2,3,5);imshow(large_bw1);title('after removing small clusters')
subplot(2,3,6);imshow(label2rgb(labels1));title('labeled clusters')

figure(2)
subplot(2,3,[1 4]);imshow(cluster2);title('original image (grayscale)')
subplot(2,3,2);imshow(bw2);title('bw after threshold')
subplot(2,3,3);imshow(fill_bw2);title('after fill')
subplot(2,3,5);imshow(large_bw2);title('after removing small clusters')
subplot(2,3,6);imshow(label2rgb(labels2));title('labeled clusters')

%the number of figures is equal to the number of labels. since labels are
%numbered 1 to NUM_LABELS doing max will get us the number of dots
num_in_cluster1 = max(labels1(:))
num_in_cluster2 = max(labels2(:))

和matlab桌面输出

num_in_cluster1 = 243
num_in_cluster2 = 51

和结果图像 enter image description here

如何在K-means分割后计算对象

1 个答案: