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Differentiate between oval and circle shaped images using MATLAB

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Neelima Dahal
Neelima Dahal 2021 年 9 月 20 日
回答済み: Image Analyst 2021 年 9 月 21 日
I am using the following code to try to differentiate two types of cells. One is circular, and the other is oval to oblong. I have about 100 images of each cell type, and have attached one each .jpg image to this question.
When I run the following code for Ck1.jpg, the rgb2gray image shows that my cell no longer has a continuous boundary. As a result, the code cannot tell what shape this cell has. Unfortunately, all the images I have are not high enough resolution. I was wondering if there was someway to interpolate the cell boundary based on however much is preserved after converting rgb image to gray.
I would greatly appreciate any help or feedback. Thank you!
clear; close all; clc;
ipath = 'D:\3) Candida yeast measurement\4) Multi Frequency Measurement - 100 Data Points\5) Ca versus Ck\';
fid = 'Ck1.jpg';
image_read = imread(strcat(ipath,fid));
figure (1)
image_RGB = imshow(image_read);
image_gray = rgb2gray(image_read);
B_W = imbinarize(image_gray);
figure (2)
image_B_W = imshow(B_W);
BW = bwareaopen(B_W,10);
figure (3)
image_BW = imshow(BW);
[B,L] = bwboundaries(BW,'holes');
figure (6)
imshow(label2rgb(L,@jet,[.5 0 .5]))
hold on
for k = 1:length(B)
boundary = B{k};
plot(boundary(:,2),boundary(:,1),'w','LineWidth',2)
end
stats = regionprops(L,'Area','Centroid');
threshold = 0.95;
% loop over the boundaries
for k = 1:length(B)
% obtain (X,Y) boundary coordinates corresponding to label 'k'
boundary = B{k};
% compute a simple estimate of the object's perimeter
delta_sq = diff(boundary).^2;
perimeter = sum(sqrt(sum(delta_sq,2)));
% obtain the area calculation corresponding to label 'k'
area = stats(k).Area;
% compute the roundness metric
metric = 4*pi*area/perimeter^2;
% display the results
metric_string = sprintf('%2.2f',metric);
% mark objects above the threshold with a black circle
if metric > threshold
metric = 0;
% centroid = stats(k).Centroid;
% plot(centroid(1),centroid(2),'ko');
end
text(boundary(1,2)-35,boundary(1,1)+13,metric_string,'Color','r',...
'FontSize',14,'FontWeight','bold')
end

回答 (2 件)

Simon Chan
Simon Chan 2021 年 9 月 20 日
You may try function bwconvhull by adding the following 2 lines.
However, I am not sure how robust it is for other pictures.
B_W = imbinarize(image_gray);
CH = bwconvhull(~B_W); % use function bwconvhull
B_W = ~CH; % Convert back to B_W
  1 件のコメント
Neelima Dahal
Neelima Dahal 2021 年 9 月 20 日
Thank you for responding to my question. While your solution works perfectly for Ck1.jpg file, it does not for the other attached image Ca1.jpg. I also tried it on other oval cell images I have, but it doesn't work for them either.

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Image Analyst
Image Analyst 2021 年 9 月 21 日
Assuming you can get a mask for the blobs, use the 'Eccentricity' option in regionprops
props = regionprops(mask, 'Eccentricity');
Alternatively use bwferet().

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