Trying to segment out the lung region, I am having a lot of trouble. Incoming image is like this: (This is essentially a jpg conversion, and each pixel is 8 bits.)
I = dicomread('000019.dcm');
I8 = uint8(I / 256);
B = im2bw(I8, 0.007);
segmented = imclearborder(B);
Above script generates:
Q-1
I am interested in entire inner black part with white matter as well. I have started matlab couple of days ago, so not quite getting how can I do it. If it is not clear to you what kind of output I want, let me know-I will upload an image. But I think there is no need.
Q-2
in B = im2bw(I8, 0.007); why I need to give a threshold so low? with higher thresholds everything is white or black. I have read the documentation and as I understand it, the pixels with value less than 0.007 are marked black and everything above is white. Is it because of my 16-to-8 bit conversion?
An other automatic solution that I did quickly using ImageJ (there are the same algorithms in MatLab):
Automatic thresholding using Huang or Li in the color space of your choice (all of them work)
Opening with a structuring element of type disk (delete the small components)
Connected components labeling.
Delete the components that touches the border of the images.
Fill holes.
And you have a clean result.
Here's a working solution in python using OpenCV:
import cv2 #openCV
import numpy as np
filename = 'zFrkx.jpg' #name of file in quotations here... assumes file is in same dir as .py file
img_gray = cv2.imread(filename, 0) #converts jpg image to grayscale representation
min_val = 100 #try shifting these around to expand or collapse area of interest
max_val = 150
ret, lung_mask = cv2.threshold(img_gray, min_val, max_val, cv2.THRESH_BINARY_INV) #fixed threshold uses values you'll def above
lung_layer = cv2.bitwise_and(img_gray, img_gray, mask = lung_mask)
cv2.imwrite('cake.tif', lung_layer) #outputs desired layer to current working dir
I tried running the script with threshold values set arbitrarily to 100,150 and got the following result, from which you could select the largest continuous element using dilation and segmentation techniques (http://docs.opencv.org/master/d3/db4/tutorial_py_watershed.html#gsc.tab=0).
Also, I suggest you crop the bottom and top X pixels to cut out text since no lung will fill the top or bottom of the picture.
Use tif instead of jpg format to avoid compression related artifact.
I know you noted that you'd like the medullar(?) white matter, too. Would be glad to help with that, but could you first explain in plain english how your shared matlab code works? Seems to work pretty well for the WM.
Hope this helps!
Related
I had to recover a hard drive and a lot of photos in it came out corrupted. I'm talking about 200.000 photos. I already wrote a script that finds corrupted JPEGs. But some of these images are not corrupted on a file format level. Yet they appear as the example I am showing. The grey part i suspect is data missing from the file. The grey part size is variable and sometimes it has an incomplete line in it.
So I'm thinking I could write or find a script that finds grey rectangles in these images.
How do I do this? Something that opens the image data and looks for this giant grey rectangle? I have no idea where to start. I can code in a bunch of languages.
Any help/examples, is much appreciated.
I was thinking that the grey rectangle is always the same colour, so I created a function to see if that grey is one of the top 10 most frequent
colours.
If the colour had changed, then I would have adjusted the code accordingly to check if the top colour is at least 10x more frequent than the second most frequent colour.
Didn't have to learn feature detection this time. Shame. :(
from collections import Counter
from PIL import Image
# Open the image file
image = Image.open(file)
# Convert the image to RGB format (if it's not already)
image = image.convert('RGB')
# Get a list of all the pixels in the image
pixels = list(image.getdata())
# Count the number of pixels with each RGB value
counts = Counter(pixels)
most_common_colors = counts.most_common(10)
return (128,128,128) in [t[0] for t in most_common_colors]
For my master thesis I have to examine several similarly structured images with Matlab. I have already coded the actual evaluation. My problem is that the images have a black frame of different thickness. This will cause the evaluation to be falsified. So far I cut the border by hand. So I wanted to ask if someone could help me cut this frame using a Matlab code.
Below is an example picture,
I tried to solve this problem with the edge-plug in (https://de.mathworks.com/help/images/ref/edge.html) but it didn´t worked.
Maybe you can give me some advice
I wouldn't be using the edge detection as you have linked to in your question.
Rather, I would focus on reducing the dimensions of the image and analyzing the "reduced image". In the following code you can see an example of reducing the image into two representative lines, and analyzing the line to detect the specimen.
% Load the image.
Img = imread('pfi36.png');
% Level to pass for the "edge" detection.
lvl = 50;
% Add a buffer around the edges.
buf = 10;
% Reduce the Image to 1D along the x and y driections respectively.
lrData = mean(Img,1);
tdData = mean(Img,2);
% Find the indices of the edges.
indL = find(lrData>lvl,1,'first');
indR = find(lrData>lvl,1,'last');
indB = find(tdData>lvl,1,'last');
% Show the Cropped image.
newImg = Img(1:(indB+buf),(indL-buf):(indR+buf));
imshow(newImg);
I am a new user on image processing via Matlab. My first aim is applying the article and comparing my results and authors' results.
The article can be found here: http://arxiv.org/ftp/arxiv/papers/1306/1306.0139.pdf
First problem, Image Quality: In Figure 7, masks are defined but I couldn't reach the mask data set, and I use the screenshot so image quality is low. In my view, it can effect the results. Is there any suggestions?
Second problem, Merging images: I want to apply mask 1 on the Lena. But I don't want to use paint =) On the other hand, is it possible merging the images and keeping the lena?
You need to create the mask array. The first step is probably to turn your captured image from Figure 7 into a black and white image:
Mask = im2bw(Figure7, 0.5);
Now the background (white) is all 1 and the black line (or text) is 0.
Let's make sure your image of Lena that you got from imread is actually grayscale:
LenaGray = rgb2gray(Lena);
Finally, apply your mask on Lena:
LenaAndMask = LenaGray.*Mask;
Of course, this last line won't work if Lena and Figure7 don't have the same size, but this should be an easy fix.
First of all, You have to know that this paper is published in archive. when papers published in archive it is always a good idea to know more about the author and/or the university that published the paper.
TRUST me on that: you do not need to waste your time on this paper.
I understand your demand: but it is not a good idea to do get the mask by doing print screen. The pixel values that can be achieved by using print screen may not be the same as the original values. The zoom may change the size. so you need to be sure that the sizes are the same.
you can do print screen. past the image.
crop the mask.
convert rgb to gray scale.
threshold the gray scale to get the binary.
if you saved the image as jpeg. distortions because of high frequency edges will change edge shape.
My short question
How to detect the black dots in the following images? (I paste only one test image to make the question look compact. More images can be found →here←).
My long question
As shown above, the background color is roughly blue, and the dots color is "black". If pick one black pixel and measure its color in RGB, the value can be (0, 44, 65) or (14, 69, 89).... Therefore, we cannot set a range to tell the pixel is part of the black dot or the background.
I test 10 images of different colors, but I hope I can find a method to detect the black dots from more complicated background which may be made up of three or more colors, as long as human eyes can identify the black dots easily. Some extremely small or blur dots can be omitted.
Previous work
Last month, I have asked a similar question at stackoverflow, but have not got a perfect solution, some excellent answers though. Find more details about my work if you are interested.
Here are the methods I have tried:
Converting to grayscale or the brightness of image. The difficulty is that I can not find an adaptive threshold to do binarization. Obviously, turning a color image to grayscale or using the brightness (HSV) will lose much useful information. Otsu algorithm which calculates adaptive threshold can not work either.
Calculating RGB histogram. In my last question, natan's method is to estimate the black color by histogram. It is time-saving, but the adaptive threshold is also a problem.
Clustering. I have tried k-means clustering and found it quite effective for the background that only has one color. The shortage (see my own answer) is I need to set the number of clustering center in advance but I don't know how the background will be. What's more, it is too slow! My application is for real time capturing on iPhone and now it can process 7~8 frames per second using k-means (20 FPS is good I think).
Summary
I think not only similar colors but also adjacent pixels should be "clustered" or "merged" in order to extract the black dots. Please guide me a proper way to solve my problem. Any advice or algorithm will be appreciated. There is no free lunch but I hope a better trade-off between cost and accuracy.
I was able to get some pretty nice first pass results by converting to HSV color space with rgb2hsv, then using the Image Processing Toolbox functions imopen and imregionalmin on the value channel:
rgb = imread('6abIc.jpg');
hsv = rgb2hsv(rgb);
openimg = imopen(hsv(:, :, 3), strel('disk', 11));
mask = imregionalmin(openimg);
imshow(rgb);
hold on;
[r, c] = find(mask);
plot(c, r, 'r.');
And the resulting images (for the image in the question and one chosen from your link):
You can see a few false positives and missed dots, as well as some dots that are labeled with multiple points, but a few refinements (such as modifying the structure element used in the opening step) could clean these up some.
I was curios to test with my old 2d peak finder code on the images without any threshold or any color considerations, really crude don't you think?
im0=imread('Snap10.jpg');
im=(abs(255-im0));
d=rgb2gray(im);
filter=fspecial('gaussian',16,3.5);
p=FastPeakFind(d,0,filter);
imagesc(im0); hold on
plot(p(1:2:end),p(2:2:end),'r.')
The code I'm using is a simple 2D local maxima finder, there are some false positives, but all in all this captures most of the points with no duplication. The filter I was using was a 2d gaussian of width and std similar to a typical blob (the best would have been to get a matched filter for your problem).
A more sophisticated version that does treat the colors (rgb2hsv?) could improve this further...
Here is an extraodinarily simplified version, that can be extended to be full RGB, and it also does not use the image procesing library. Basically you can do 2-D convolution with a filter image (which is an example of the dot you are looking for), and from the points where the convolution returns the highest values, are the best matches for the dots. You can then of course threshold that. Here is a simple binary image example of just that.
%creating a dummy image with a bunch of small white crosses
im = zeros(100,100);
numPoints = 10;
% randomly chose the location to put those crosses
points = randperm(numel(im));
% keep only certain number of points
points = points(1:numPoints);
% get the row and columns (x,y)
[xVals,yVals] = ind2sub(size(im),points);
for ii = 1:numel(points)
x = xVals(ii);
y = yVals(ii);
try
% create the crosses, try statement is here to prevent index out of bounds
% not necessarily the best practice but whatever, it is only for demonstration
im(x,y) = 1;
im(x+1,y) = 1;
im(x-1,y) = 1;
im(x,y+1) = 1;
im(x,y-1) = 1;
catch err
end
end
% display the randomly generated image
imshow(im)
% create a simple cross filter
filter = [0,1,0;1,1,1;0,1,0];
figure; imshow(filter)
% perform convolution of the random image with the cross template
result = conv2(im,filter,'same');
% get the number of white pixels in filter
filSum = sum(filter(:));
% look for all points in the convolution results that matched identically to the filter
matches = find(result == filSum);
%validate all points found
sort(matches(:)) == sort(points(:))
% get x and y coordinate matches
[xMatch,yMatch] = ind2sub(size(im),matches);
I would highly suggest looking at the conv2 documentation on MATLAB's website.
I want to process an image in matlab
The image consists out of a solid back ground and two specimens (top and bottom side). I already have a code that separate the top and bottom and make it two images. But the part what I don't get working is to crop the image to the glued area only (red box in the image, I've only marked the top one). However, the cropped image should be a rectangle just like the red box (the yellow background, can be discarded afterwards).
I know this can be done with imcrop, but this requires manual input from the user. The code needs to be automated such that it is possible to process more images without user input. All image will have the same colors (red for glue, black for material).
Can someone help me with this?
edit: Thanks for the help. I used the following code to solve the problem. However, I couldn't get rid of the black part right of the red box. This can be fix by taping that part off before making pictures. The code which I used looks a bit weird, but it succeeds in counting the black region in the picture and getting a percentage.
a=imread('testim0.png');
level = graythresh(a);
bw2=im2bw(a, level);
rgb2=bw2rgb(bw2);
IM2 = imclearborder(rgb2,4);
pic_negative = ait_imgneg(IM2);
%% figures
% figure()
% image(rgb2)
%
% figure()
% imshow(pic_negative)
%% Counting percentage
g=0;
for j=1:size(rgb2,2)
for i=1:size(rgb2,1)
if rgb2(i,j,1) <= 0 ...
& rgb2(i,j,2) <= 0 ...
& rgb2(i,j,3) <= 0
g=g+1;
end
end
end
h=0;
for j=1:size(pic_negative,2)
for i=1:size(pic_negative,1)
if pic_negative(i,j)== 0
h=h+1;
end
end
end
per=g/(g+h)
If anyone has some suggestions to improve the code, I'm happy to hear it.
For a straight-forward image segmentation into 2 regions (background, foreground) based on color (yellow, black are prominent in your case), an option can be clustering image color values using kmeans algorithm. For additional robustness you can transform the image from RGB to Lab* colorspace.
The example for your case follows the MATLAB Imape Processing example here.
% read and transform to L*a*b space
im_rgb = double(imread('testim0.png'))./256;
im_lab = applycform(im_rgb, makecform('srgb2lab'));
% keep only a,b-channels and form feature vector
ab = double(lab_I(:,:,2:3));
[nRows, nCols, ~] = size(ab);
ab = reshape(ab,nRows * nCols,2);
% apply k-means for 2 regions, repeat c times, e.g. c = 5
nRegions = 2;
[cluster_idx cluster_center] = kmeans(ab,nRegions, 'Replicates', 5);
% get foreground-background mask
im_regions = reshape(cluster_idx, nRows, nCols);
You can use the resulting binary image to index the regions of interest (or find the boundary) in the original reference image.
Images are saved as matrices. If you know the bounds in pixels of the crop box want to crop you can execute the crop using indexing.
M = rand(100); % create a 100x100 matrix or load it from an image
left = 50;
right = 75;
top = 80;
bottom = 10;
croppedM = M(bottom:top, left:right);
%save croppedm
You can easily get the unknown bounded crop by
1) Contour plotting the image,
2) find() on the result for the max/min X/ys,
3) use #slayton's method to perform the actual crop.
EDIT: Just looked at your actual image - it won't be so easy. But color enhance/threshold your image first, and the contours should work with reasonable accuracy. Needless to say, this requires tweaking to your specific situation.
since you've already been able to seperate the top and bottom, and also able to segment the region you want (including the small part of the right side that you don't want), I propose you just add a fix at the end of the code via the following.
after segmentation, sum each column Blue intensity value, so that you are compressing the image from 2d to 1d. so if original region is width=683 height=59, the new matrix/image will be simply width=683 height=1.
now, you can apply a small threshold to determine where the edge should lie, and apply a crop to the image at that location. Now you get your stats.