我怎样才能检测到半导体图像中的缺陷？

Question

所以我有一个半导体晶片的图像，它有一个缺陷，我需要用Matlab来检测。我只应该探测到它，而不是它的背景。我还要测量它的周长和面积。

到目前为止，我有这样的代码，我将原始图像转换为二值图像，然后对其使用扩展，然后尝试得到它的轮廓。当得到周边和面积，我收到的结果不仅是因为缺陷，但其他的图像，这不是我想要的。如何只提取缺陷，才能得到它的面积和参数。

图片：

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fig3 = imread('figure3.png');
imshow(fig3);

title('Original image', 'FontSize', 18);
%Gray image
fig3Gray = rgb2gray(fig3);



%Binary image
BW3 = imbinarize(fig3Gray,0.5);
imshow(BW3);
title('Binary image', 'FontSize', 18);

se3 = strel('square',5);

%Dilation image
dilated3 = imdilate(BW3,sr);
imshow(dilated3);
title('Dilated image', 'FontSize', 18);

minus3 = ~(BW3-dilated3);
imshow(minus3);
title('Contour image', 'FontSize', 18);
imshowpair(minus3,BW3,'montage');

%Perimeter and Area calculation
Coon3 = bwconncomp(~BW3)
ANS3 = length(Coon3.PixelIdxList{1});
OUTPUT3 = regionprops(Coon3,'Perimeter','Area');
P3 = OUTPUT3.Perimeter
Area3 = OUTPUT3.Area

Answer 1

这个问题比你的previous question难得多。

• 下面的解决方案使用一种迭代方法(两次迭代)。
• 包含了关于集群与其邻居之间的区别的启发式方法。
• 包含一个启发式，即集群不能太高或太长。

请阅读评论：

clear

fig3 = imread('figure3.png');
fig3Gray = rgb2gray(fig3);
fig3Gray = im2double(fig3Gray); %Convert from uint8 to double (MATLAB math works in double).

%figure;imshow(fig3Gray);

%Apply median filter with large radius.
Med = medfilt2(fig3Gray, [51, 51], 'symmetric');
%figure;imshow(Med);

D = abs(fig3Gray - Med);
%figure;imshow(D);impixelinfo

BW = imbinarize(D, 0.3);
%figure;imshow(BW);impixelinfo

Coon = bwconncomp(BW);

fig3GrayMasked = fig3Gray;

%Cover the tall clusters and the long clusters.
for i = 1:length(Coon)
    C = Coon.PixelIdxList{i}; %Cluster coordinates.
    [Y, X] = ind2sub(size(fig3Gray), C); %Convert to x,y indices.
    is_tall = (max(Y) - min(Y)) > 50; %true if cluster is tall.
    is_wide = (max(X) - min(X)) > 50; %true if cluster is wide.

    %Replace tall and long clusters by pixels from median image.
    if ((is_tall) || (is_wide))
        fig3GrayMasked(C) = Med(C);
    end
end

%figure;imshow(fig3GrayMasked);impixelinfo

%Second iteration: search largest cluster on fig3GrayMasked image.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Med = medfilt2(fig3GrayMasked, [51, 51], 'symmetric');

D = abs(fig3GrayMasked - Med);
%figure;imshow(D);impixelinfo

BW = imbinarize(D, 0.3);
%figure;imshow(BW);impixelinfo

Coon = bwconncomp(BW);

%Find index of largest cluster in list of clusters Coon.PixelIdxList
[~, i] = max(cellfun(@numel, Coon.PixelIdxList));

%Get the indices of the largest cluster
C = Coon.PixelIdxList{i};
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%Paint cluster in yellow color (just for fun).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
BW = zeros(size(BW), 'logical');
BW(C) = 1;
Y = im2uint8(cat(3, ones(size(BW)), ones(size(BW)), zeros(size(BW))));
fig3(cat(3, BW, BW, BW)) = Y(cat(3, BW, BW, BW));
figure;imshow(fig3)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

结果：

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Answer 2

让我们从读取图像并将其转换为二进制开始。请注意，我降低了门槛，以消除不必要的细节。

clear; close all; clc

fig3 = imread('XEQ59.png');
imshow(fig3);

title('Original image', 'FontSize', 18);
%Gray image
fig3Gray = rgb2gray(fig3);
%Binary image
BW3 = imbinarize(fig3Gray, 0.2); % lowered threshold
figure; imshow(BW3)
title('binary image')

​

​

现在我们继续寻找缺陷的坐标。为此，我们定义了一个结构元素，它定义了我们想要的形状se。

​

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我们正在寻找与se匹配的图像中的部分。若要匹配给定的坐标，其周围区域必须是se。

注意，这里的灰色值被忽略了，它们可以是白色的，也可以是黑色的。

se是手动定义的，其中1表示白色，-1表示黑色，0表示被忽略的像素。

% hit-miss
se = [1, 1, -1*ones(1,5), ones(1, 3); ...
      ones(6,1), -1*ones(6), zeros(6,2), ones(6,1); ...
      ones(3,2), zeros(3,1), -1*ones(3,6), ones(3,1)];
figure; imshow(uint8(255/2*(se+1)), 'InitialMagnification', 3000)
title('structuring element')

应用命中-误操作找到缺陷的位置：

pos = bwhitmiss(BW3, se);
figure; imshow(pos)
title('position of defect')
input('Press enter to continue...')

既然我们有了这个位置，我们就会把这个特定的像素位置增大，直到它不再生长，以获得缺陷。

% get the defect
close all; clc
def = pos;
last_def = zeros(size(def));
while ~isequal(def, last_def)
    last_def = def;
    def = ~BW3 & imdilate(def, ones(3));
    imshow(def)
    title('defect')
    pause(0.1)
end

​

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计算面积和周长：

% area
area = sum(def(:))

% perimeter
vert = imdilate(def, [1; 1; 1]) - def;
horz = imdilate(def, [1 1 1]) - def;
perimeter = sum(vert(:)) + sum(horz(:))

area =
   102
perimeter =
    54