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分水岭算法设置删除所有连接的组件

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我正在使用分水岭算法来尝试分割触摸核 . 典型图像可能如下所示：
enter image description here
或者：
enter image description here

我正在尝试使用此代码应用分水岭算法：

show(RGB_img)


%Convert to grayscale image
I = rgb2gray(RGB_img);

%Take structuring element of a disk of size 10, for the morphological transformations
%Attempt to subtract the background from the image: top hat is the
%subtraction of the open image from the original


%Morphological transformation to subtract background noise from the image
%Tophat is the subtraction of an opened image from the original. Remove all
%images smaller than the structuring element of 10
I1 = imtophat(I, strel('disk', 10));

%Increases contrast
I2 = imadjust(I1);
%show(I2,'contrast')
%Assume we have background and foreground and assess thresh as such 
level = graythresh(I2);
%Convert to binary image based on graythreshold
BW = im2bw(I2,level);
show(BW,'C');



BW = bwareaopen(BW,8);
show(BW,'C2');

BW = bwdist(BW) <= 1;
show(BW,'joined');
%Complement because we want image to be black and background white
C = ~BW;
%Use distance tranform to find nearest nonzero values from every pixel
D = -bwdist(C);

%Assign Minus infinity values to the values of C inside of the D image
%   Modify the image so that the background pixels and the extended maxima
%   pixels are forced to be the only local minima in the image (So you could
%   hypothetically fill in water on the image

D(C) = -Inf;

%Gets 0 for all watershed lines and integers for each object (basins)
L = watershed(D);
show(L,'L');

%Takes the labels and converts to an RGB (Using hot colormap)
fin = label2rgb(L,'hot','w');

% show(fin,'fin');
im = I;

%Superimpose ridgelines,L has all of them as 0 -> so mark these as 0(black)
im(L==0)=0;

clean_img = L;
show(clean_img)

无论出于何种原因_1112820_整个图像变暗 . 这个相同的代码块已经处理了一些其他图像，所有这些图像都更像是这些图像 . 但是，我以为我用 BW = bwdist(BW) <= 1; 补偿了这一点 . 我真的知道发生了什么 . 任何帮助都会很棒！

PS . 这是 BW = bwareaopen(BW,8);
Image description here
之后的图片

matlab image-processing image-segmentation watershed

1 回答

  • 0

    在礼帽之前，您应该执行关闭和打开以减少噪音 .

    如果在嘈杂的图像上执行区域打开，最终可能会在黑白图像上显示结果 .

    所以它会是：

    • 关闭并开放

    • Top-Hat

    • 如果仍有需要开放区域

    • 阈值

    • 侵蚀和膨胀分别找到内部和外部标记

    • 流域（从不使用没有标记的分水岭） .

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