How to identify particles in image

Question

Isaiah Stefan Engle 2017-5-15

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/340412-how-to-identify-particles-in-image

评论： Indrajit Nandi 2021-3-31

I have been working on this project for quite some time and am now seeking some outside opinions. I must create a script that will identify each of the small particles. I have looked at multiple threshold techniques and not many seem to be working. For example, I tried turning it into a binary image and getting rid of noise using the function bwareaopen, but there is still a lot of noise and the imfindcircles function is being thrown off. Any fresh ideas to identify these beads would be very helpful. Accuracy is a must in this project! The small beads are the ones I'm identifying, they are the ones that have the most consistent size in the image.

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Image Analyst 2017-5-15

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/340412-how-to-identify-particles-in-image#answer_267155

在 MATLAB Online 中打开

Have you tried a Bottom Hat Filter, imbothat()?

rgbImage = imread('StackOverflow#1.JPG');
subplot(2,2,1);
grayImage = rgb2gray(rgbImage);
imshow(grayImage, []);
axis on;
subplot(2,2,2);
se = strel('disk', 4, 0);
filteredImage = imbothat(grayImage, se);
imshow(filteredImage, []);
axis on;
% Histogram
subplot(2,2,3);
histogram(filteredImage, 256);
grid on;
xticks(0:16:255);
% Threshold
binaryImage = filteredImage > 40;
subplot(2,2,4);
imshow(binaryImage);

8 个评论
显示 6更早的评论隐藏 6更早的评论

Image Analyst 2017-5-17

在 MATLAB Online 中打开

Here is what I get with this code:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
clear;  % Erase all existing variables.
workspace;  % Make sure the workspace panel is showing.
format longg;
format compact;
fontSize = 20;
rgbImage = imread('StackOverflow#1.JPG');
subplot(2,2,1);
grayImage = rgb2gray(rgbImage);
imshow(grayImage, []);
axis on;
title('Original Gray Scale Image', 'FontSize', fontSize);
subplot(2,2,2);
se = strel('disk', 4, 0);
filteredImage = imbothat(grayImage, se);
imshow(filteredImage, []);
axis on;
title('Bottom Hat Filtered Image', 'FontSize', fontSize);
% Histogram
subplot(2,2,3);
histogram(filteredImage, 256);
grid on;
xticks(0:16:255);
title('Histogram of Gray Levels', 'FontSize', fontSize);
xlabel('Gray Level', 'FontSize', fontSize);
ylabel('Pixel Count', 'FontSize', fontSize);
% Threshold
binaryImage = filteredImage > 40;
subplot(2,2,4);
imshow(binaryImage);
title('Binary Image', 'FontSize', fontSize);
% Label the image
labeledImage = bwlabel(binaryImage);
% Measure the areas
props = regionprops(labeledImage, 'Area');
allAreas = [props.Area];
% Show histogram of blob areas:
figure;
histogram(allAreas);
grid on;
title('Histogram of Blob Areas', 'FontSize', fontSize);
xlabel('Area', 'FontSize', fontSize);
ylabel('Blob Count', 'FontSize', fontSize);

You can see the distribution of blob areas. What is a "bonded circles"? Can it be identified as a certain area? Like if the area is more than 10 pixels, it's 2 blobs connected together instead of one single blob? What are "bonded circles"?

Isaiah Stefan Engle 2017-5-17

I also tried this approach earlier. The problem is that there are also dust particles that have around the same area of pixels. For example, the first image displays two separate dimers and 5 single beads. The second image displays a dust particle that has the same area of pixels as the bonded beads. The method you are suggesting is unfortunately unable to distinguish the two.