How to remove skin parts on segmentation ?
First, I made this first picture smaller, since the picture is somehow intimidating, I'll give the image at the end section.
I'm using RGB & ycbcr segmentation, but it seems the segmentation didn't work well.
clear all;
close all;
clc;
img=imread('acne.jpg');
%ycbcr segmentation
img_ycbcr=img; %image from the previous segmentation
ycbcr=rgb2ycbcr(img_ycbcr);
cb=ycbcr(:,:,2);
cr=ycbcr(:,:,3);
%Detect Skin
%[r,c,v] = find(cb>=77 & cb<=127 & cr>=133 & cr<=173);
[r c v] = find(cb<=77 | cb >=127 | cr<=133 | cr>=173);
numid = size(r,1);
%Mark Skin Pixels
for i=1:numid
img_ycbcr(r(i),c(i),:) = 0;
% bin(r(i),c(i)) = 1;
end
figure
title('ycbcr segmentation');
imshow(img_ycbcr);
%==============================================================
%rgb segmentation
img_rgb=img_ycbcr;
r=img_rgb(:,:,1);
g=img_rgb(:,:,2);
b=img_rgb(:,:,3);
[row col v]= find(b>0.79*g-67 & b<0.78*g+42 & b>0.836*g-14 & b<0.836*g+44 ); %non skin pixels
numid=size(row,1);
for i=1:numid
img_rgb(row(i),col(i),:)=0;
end
figure
imshow(img_rgb);
Here my sample :
I agree with Adriaan. Don't do it with just colour, use additional information such as the shape and the edges.
The last two colorplanes seem to have the most contrast, so let's use one of them:
Nipple = imread('N8y6Q.jpg')
Nipple = imadjust(Nipple(:,:,2));
imshow(Nipple)
[centers, radii] = imfindcircles(Nipple, [30,60]);
hold on
imshow(Nipple);
viscircles(centers, radii);
The circular Hough transform is a robust way to find circular objects if you know the approximate radius range and are satisfied with the approx. location and size of the object.
If not you can try other classical methods, e.g. (Canny) edge detection, using the Hough center point as a marker -> region growing, fitting a snake etc. etc.
Related
I tried to use image processing toolbox to count my colony. I used imfindcircles to find the colony and count. But I got some problems:
(1)Due to that my colony could be white and black, I tried to find the colony by using Bright or Dark in ObjectPolarity, and use a if loop to select which one I finally choose. But the first step of my if loop doesn't really work.
(2) For using imfindcircles to find the circle, I found it works for the colony in white, while the method is a disaster for the black colony. I'm kind of desperate now because I can't find other ways to segment the colony.
So finally i need to: label each colony in the plate, count the colony number, calculate each colony size, extract the mean gray value for each colony (the colour).
Thank you very much!!!
So here is my code:
im = imread(test.jpeg)
imshow(im)
% to find the colony
[centersBright, radiiBright] = imfindcircles(im,[30 60],'ObjectPolarity','bright','Sensitivity',0.925,'Method','twostage','EdgeThreshold',0.1)
[centersDark, radiiDark] = imfindcircles(im,[30 60],'ObjectPolarity','dark','Sensitivity',0.925,'Method','twostage','EdgeThreshold',0.15)
% to select which one is the correct count. if one of the length(centres) value is lower than 10, I consider it as an error. But if both length(centres) is low than 10, I consider it as a treatment effect and accept the value.
if length(centersDark)<10<length(centersBright)
centers=centersBright
radii=radiiBright
elseif length(centersBright)<10<length(centersDark)
centers=centersDark
radii=radiiDark
else
centers=[centersBright,centersDark]
radii=[radiiBright,radiiDark]
end
% view and label the colony
h = viscircles(centers,radii)
for k = 1:length(radii)
string = sprintf('%d',k)
text(centers(k,1),centers(k,2),string,'color','y','HorizontalAlignment', 'center','VerticalAlignment', 'middle')
area(k)=pi*radii(k)^2
end
Suggested Solution
While it's hard to distinguish between the colonies and their surrounding in the black and white version of your input, it is not hard to do so in the hue space by using thresholding.
The reason is that the colonies have a unique hue which is different from their background.
This will be more noticable after converting to HSV space:
Therefore, I suggest the folowing solution:
convert input image to HSV space
use thresholding on the hue component.
extract connected components
Use the connected components which are large enough for the mask
perform imclose opertation for cleaning the artifacts
Code
%reads the image
I = imread('colony2.png');
%convert to hsv
hsvIm = rgb2hsv(I);
%thresholding on the hue space
bwIm = hsvIm(:,:,1) < 0.15;
%find connected components
CC = bwconncomp(bwIm);
%choose only cc with sufficient amount of pixels
numPixels = cellfun(#numel,CC.PixelIdxList);
relevantCC = CC.PixelIdxList(numPixels > 10);
%generate a binary mask with these conencted componants
colonyMask = false(size(bwIm));
for ii=1:length(relevantCC)
colonyMask(relevantCC{ii}) = true;
end
%perform morpholopical operations for cleaning
colonyMask = imclose(colonyMask,strel('disk',1));
%display result
h = imshow(I); % Save the handle; we'll need it later
set(h, 'AlphaData', ~colonyMask);
Results
Selection of threshold
The threshold was chosen by choosing the first pick in the histogram of the hue component
histogram(hsvIm(:,:,1))
I'm working on images with overlapping line shapes (left plot). Ultimately I want to segment single objects. I'm working with a Hough transform to achieve this and it works well in finding lines of (significantly) different orientation - e.g. represented by the two maxima in the hough space below (middle plot).
the green and yellow lines (left plot) and crosses (right plot) stem from an approach to do something with the thickness of the line. I couldn't figure out how to extract a broad line though, so I didn't follow up.
I'm aware of the ambiguity of assigning the "overlapping pixels". I will address that later.
Since I don't know, how many line objects one connected region may contain, my idea is to iteratively extract the object corresponding to the hough line with the highest activation (here painted in blue), i.e. remove the line shaped object from the image, so that the next iteration will find only the other line.
But how do I detect, which pixels belong to the line shaped object?
The function hough_bin_pixels(img, theta, rho, P) (from here - shown in the right plot) gives pixels corresponding to the particular line. But that obviously is too thin of a line to represent the object.
Is there a way to segment/detect the whole object that is orientied along the strongest houghline?
The key is knowing that thick lines in the original image translate to wider peaks on the Hough Transform. This image shows the peaks of a thin and a thick line.
You can use any strategy you like to group all the pixels/accumulator bins of each peak together. I would recommend using multithresh and imquantize to convert it to a BW image, and then bwlabel to label the connected components. You could also use any number of other clustering/segmentation strategies. The only potentially tricky part is figuring out the appropriate thresholding levels. If you can't get anything suitable for your application, err on the side of including too much because you can always get rid of erroneous pixels later.
Here are the peaks of the Hough Transform after thresholding (left) and labeling (right)
Once you have the peak regions, you can find out which pixels in the original image contributed to each accumulator bin using hough_bin_pixels. Then, for each peak region, combine the results of hough_bin_pixels for every bin that is part of the region.
Here is the code I threw together to create the sample images. I'm just getting back into matlab after not using it for a while, so please forgive the sloppy code.
% Create an image
image = zeros(100,100);
for i = 10:90
image(100-i,i)=1;
end;
image(10:90, 30:35) = 1;
figure, imshow(image); % Fig. 1 -- Original Image
% Hough Transform
[H, theta_vals, rho_vals] = hough(image);
figure, imshow(mat2gray(H)); % Fig. 2 -- Hough Transform
% Thresholding
thresh = multithresh(H,4);
q_image = imquantize(H, thresh);
q_image(q_image < 4) = 0;
q_image(q_image > 0) = 1;
figure, imshow(q_image) % Fig. 3 -- Thresholded Peaks
% Label connected components
L = bwlabel(q_image);
figure, imshow(label2rgb(L, prism)) % Fig. 4 -- Labeled peaks
% Reconstruct the lines
[r, c] = find(L(:,:)==1);
segmented_im = hough_bin_pixels(image, theta_vals, rho_vals, [r(1) c(1)]);
for i = 1:size(r(:))
seg_part = hough_bin_pixels(image, theta_vals, rho_vals, [r(i) c(i)]);
segmented_im(seg_part==1) = 1;
end
region1 = segmented_im;
[r, c] = find(L(:,:)==2);
segmented_im = hough_bin_pixels(image, theta_vals, rho_vals, [r(1) c(1)]);
for i = 1:size(r(:))
seg_part = hough_bin_pixels(image, theta_vals, rho_vals, [r(i) c(i)]);
segmented_im(seg_part==1) = 1;
end
region2 = segmented_im;
figure, imshow([region1 ones(100, 1) region2]) % Fig. 5 -- Segmented lines
% Overlay and display
out = cat(3, image, region1, region2);
figure, imshow(out); % Fig. 6 -- For fun, both regions overlaid on original image
I have an image shown below:
I am applying some sort of threshold like in the code. I could separate the blue objects like below:
However, now I have a problem separating these blue objects. I applied watershed (I don't know whether I made it right or wrong) but it didn't work out, so I need help to separate these connected objects.
The code I tried to use is shown below:
RGB=imread('testImage.jpg');
RGB = im2double(RGB);
cform = makecform('srgb2lab', 'AdaptedWhitePoint', whitepoint('D65'));
I = applycform(RGB,cform);
channel1Min = 12.099;
channel1Max = 36.044;
channel2Min = -9.048;
channel2Max = 48.547;
channel3Min = -53.996;
channel3Max = 15.471;
BW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
(I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
(I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
maskedRGBImage = RGB;
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;
figure
imshow(maskedRGBImage)
In general, this type of segmentation is a serious research problem. In your case, you could do pretty well using a combination of morphology operations. These see widespread use in microscopy image processing.
First, clean up BW a bit by removing small blobs and filling holes,
BWopen = imopen(BW, strel('disk', 6));
BWclose = imclose(BWopen, strel('disk', 6));
(you may want to tune the structuring elements a bit, "6" is just a radius that seemed to work on your test image.)
Then you can use aggressive erosion to generate some seeds
seeds = imerode(BWclose, strel('disk', 35));
which you can use for watershed, or just assign each point in BW to its closest seed
labels = bwlabel(seeds);
[D, i] = bwdist(seeds);
closestLabels = labels(i);
originalLabels = BWopen .* closestLabels;
imshow(originalLabels, []);
I would try the following steps:
Convert the image to gray and then to a binary mask.
Apply morphological opening (imopen) to clean small noisy objects.
Apply Connected Component Analysis (CCA) using bwlabel. Each connected component contains at least 1 object.
These blue objects really look like stretched/distorted circles, so I would try Hough transform to detect cicles inside each labeled component. There is a built-in function (imfindcircles) or code available online (Hough transform for circles), depending on your Matlab version and available toolboxes.
Then, you need to take some decisions regarding the number of objects, N, inside each component (N>=1). I don't know in advance what the best criteria should be, but you could also apply these simple rules:
[i] An object needs to be of a minimum size.
[ii] Overlaping circles correspond to the same object (or not, depending on the overlap amount).
The circle centroids can then serve as seeds to complete the final object segmentation. Of course, if there is only one circle in each component, you just keep it directly as an object.
I didn't check all steps for validity in Matlab, but I quickly checked 1, 2, and 4 and they seemed to be quite promising. I show the result of circle detection for the most difficult component, in the center of the image:
The code I used to create this image is:
close all;clear all;clc;
addpath 'circle_hough'; % add path to code of [Hough transform for circles] link above
im = imread('im.jpg');
img = rgb2gray(im);
mask = img>30; mask = 255*mask; % create a binary mask
figure;imshow(mask)
% filter the image so that only the central part of 6 blue objects remains (for demo purposes only)
o = zeros(size(mask)); o(170:370, 220:320) = 1;
mask = mask.*o;
figure;imshow(mask);
se = strel('disk',3);
mask = imopen(mask,se); % apply morphological opening
figure;imshow(mask);
% check for circles using Hough transform (see also circle_houghdemo.m in [Hough transform for circles] link above)
radii = 15:5:40; % allowed circle radii
h = circle_hough(mask, radii, 'same', 'normalise');
% choose the 10 biggest circles
peaks = circle_houghpeaks(h, radii, 'nhoodxy', 15, 'nhoodr', 21, 'npeaks', 10);
% show result
figure;imshow(im);
for peak = peaks
[x, y] = circlepoints(peak(3));
hold on;plot(x+peak(1), y+peak(2), 'g-');
end
Some spontaneous thoughts. I assume the ultimate goal is to count the blue corpuscles. If so, I would search for a way to determine the total area of those objects and the average area of a corpuscle. As a first step convert to binary (black and White):
level = graythresh(RGB);
BW = im2bw(RGB,level);
Since morphological operations (open/close) will not preserve the areas, I would work with bwlabel to find the connected components. Looking at the picture I see 12 isolated corpuscles, two connected groups, some truncated corpuscles at the edge and some small noisy fragments. So first remove small objects and those touching edges. Then determine total area (bwarea) and the median size of objects as a proxy for the average area of one corpuscle.
I'm working in image segmentation, testing a lot of different segmentation algorithms, in order to do a comparitive study. At the moment i'm using Hough transform to find circles in the image. The images that i'm using have plenty objects, so when Í count the objects the result is hudge. I think the problem, is the overlaping circle. Do you know how can i maybe remove the overlaping circles to have a result more close to reality?
The code that i'm using is:
clear all, clc;
% Image Reading
I=imread('0001_c3.png');
figure(1), imshow(I);set(1,'Name','Original')
image used
% Gaussian Filter
W = fspecial('gaussian',[10,10]);
J = imfilter(I,W);
figure(2);imshow(J);set(2,'Name','Filtrada média');
X = rgb2gray(J);
figure(3);imshow(X);set(3,'Name','Grey');
% Finding Circular objects -- Houng Transform
[centers, radii, metric] = imfindcircles(X,[10 20], 'Sensitivity',0.92,'Edge',0.03); % [parasites][5 30]
centersStrong = centers(1:60,:); % number of objects
radiiStrong = radii(1:60);
metricStrong = metric(1:60);
viscircles(centersStrong, radiiStrong,'EdgeColor','r');
length(centers)% result=404!
You could simply loop over the circles and check if others are "close" to them. If so, you ignore them.
idx_mask = ones(size(radii));
min_dist = 1; % relative value. Tweak this if slight overlap is OK.
for i = 2:length(radii)
cur_cent = centers(i, :);
for j = 1:i-1
other_cent = centers(j,:);
x_dist = other_cent(1) - cur_cent(1);
y_dist = other_cent(2) - cur_cent(2);
if sqrt(x_dist^2+y_dist^2) < min_dist*(radii(i) + radii(j)) && idx_mask(j) == 1
idx_mask(i) = 0;
break
end
end
end
%%
idx_mask = logical(idx_mask);
centers_use = centers(idx_mask, :);
radii_use = radii(idx_mask, :);
metric_use = metric(idx_mask, :);
viscircles(centers_use, radii_use,'EdgeColor','b');
The picture shows all circles in red, and the filtered circles in blue.
The if clause checks two things:
- Are the centers of the circles closer than the sum of their radii?
- Is the other circle still on the list of considered circles?
If the answer to both questions is yes, then ignore the "current circle".
The way the loop is set up, it will keep circles that are higher up (have a lower row index). As is, the circles are already ordered by descending metric. In other words, as is this code will keep circles with a higher metric.
The code could optimized so that the loops run faster, but I don't think you'll have millions of circles in a single picture. I tried writing it in a way that it's easier to read for humans.
I want to use mathematical morphology function in MATLAB to find the boundary of can.png image. The input image is:
I want to get a boundary such as :
I tried to use different combination and parameter using strel, imerode, imdilate , But the result is not good enough (far from the expectation)
One of my trial code is:
a = imread ('can.png');
b = im2bw(a);
SE = strel('rectangle', [10 50 ]) ;
i2 = imdilate(b,SE);
figure(1); imshow(i2);
p = ones(4);
c = b - imerode(b,p);
figure(2); imshow(c);
The output is:
Can any body help me, how to create the expected image (black background with thin boundary for the can, please? Thank you very much.
Binarize on its morphological gradient, then do a dilation with an elementary SE, fill holes and finally obtain its border (trivial given the current image). This doesn't require any magical arbitrary threshold.
im = imread('can.png');
% turn image to BW
imb = (im < 220);
% fill the holes in the image
imf = imfill(imb, 'holes');
% find the edge
ed = edge(imf);
Resulting image: