I have a original gray scale image(I m using mammogram image with labels outside image).
I need to remove some objects(Labels) in that image, So i converted that grayscale image to a binary image. Then i followed the answer method provided in
How to Select Object with Largest area
Finally i extracted an Object with largest area as binary image. I want that region in gray scale for accessing and segmenting small objects within that. For example. Minor tissues in region and also should detect its edge.
**
How can i get that separated object region as grayscale image or
anyway to get the largest object region from gray scale directly
without converting to binary or any other way.?
**
(I am new to matlab. I dono whether i explained it correctly or not. If u cant get, I ll provide more detail)
If I understood you correctly, you are looking to have a gray image with only the biggest blob being highlighted.
Code
img = imread(IMAGE_FILEPATH);
BW = im2bw(img,0.2); %%// 0.2 worked to get a good area for the biggest blob
%%// Biggest blob
[L, num] = bwlabel(BW);
counts = sum(bsxfun(#eq,L(:),1:num));
[~,ind] = max(counts);
BW = (L==ind);
%%// Close the biggest blob
[L,num] = bwlabel( ~BW );
counts = sum(bsxfun(#eq,L(:),1:num));
[~,ind] = max(counts);
BW = ~(L==ind);
%%// Original image with only the biggest blob highlighted
img1 = uint8(255.*bsxfun(#times,im2double(img),BW));
%%// Display input and output images
figure,
subplot(121),imshow(img)
subplot(122),imshow(img1)
Output
If I understand your question correctly, you want to use the binary map and access the corresponding pixel intensities in those regions.
If that's the case, then it's very simple. You can use the binary map to identify the spatial co-ordinates of where you want to access the intensities in the original image. Create a blank image, then copy over these intensities over to the blank image using those spatial co-ordinates.
Here's some sample code that you can play around with.
% Assumptions:
% im - Original image
% bmap - Binary image
% Where the output image will be stored
outImg = uint8(zeros(size(im)));
% Find locations in the binary image that are white
locWhite = find(bmap == 1);
% Copy over the intensity values from these locations from
% the original image to the output image.
% The output image will only contain those pixels that were white
% in the binary image
outImg(locWhite) = im(locWhite);
% Show the original and the result side by side
figure;
subplot(1,2,1);
imshow(im); title('Original Image');
subplot(1,2,2);
imshow(outImg); title('Extracted Result');
Let me know if this is what you're looking for.
Method #2
As suggested by Rafael in his comments, you can skip using find all together and use logical statements:
outImg = img;
outImg(~bmap) = 0;
I decided to use find as it less obfuscated for a beginner, even though it is less efficient to do so. Either method will give you the correct result.
Some food for thought
The extracted region that you have in your binary image has several holes. I suspect you would want to grab the entire region without any holes. As such, I would recommend that you fill in these holes before you use the above code. The imfill function from MATLAB works nicely and it accepts binary images as input.
Check out the documentation here: http://www.mathworks.com/help/images/ref/imfill.html
As such, apply imfill on your binary image first, then go ahead and use the above code to do your extraction.
Related
here is my image
a.png
for binarization I try this code.
im=rgb2gray(I);
maxp=uint16(max(max(im)));
minp=uint16(min(min(im)));
bw=im2bw(im,(double(minp+maxp))/(1.42*255));
bw=~bw;
imm=bw;
but I need binarization by otsu.how can I get good binary output using otsu method?
plz help
thanks
MATLAB has its own implementation of Otsu thresholding called multithresh. In your case the code to obtain the segmented image should be something like this:
im=rgb2gray(I); % convert image to grayscale
thresh = multithresh(im); % find one threshold (using Otsu method)
segmented_im = imquantize(im, thresh); % segment image
imagesc(segmented_im); % show segmented image
I haven't tested it so I don't know how well it would perform on your image.
EDIT:
I tested it, and it doesn't work as expected. One of the problems is that Otsu's method works well when there is a clear bimodal distribution of the pixel intensities. This bimodality is lacking in your image. A call to imhist(im) after the grayscale conversion leads to this (comments added by me):
As you can see, the distribution is almost trimodal, and the threshold selected by multithresh is the first one, while you want the second one. The first workaround that comes to my mind (especially if all the images in your dataset are similar to the one you posted, i.e. have a similar intensity distribution) is to make multithresh output two thresholds, and then selecting the last (highest) one:
thresholds = multithresh(im, 2);
thresh = thresholds(end);
Then proceed with the segmentation of the image as stated above. This second method leads to this segmentation:
EDIT 2 (putting it all together):
Indeed the output segmented_im is not a binary image, but a label image. It's easy enough to convert it to a binary image. I will include directly all the code in this next snippet:
im=rgb2gray(I); % convert image to grayscale
thresholds = multithresh(im, 2); % find two thresholds using Otsu
thresh = thresholds(end); % select larger one
segmented_im = imquantize(im, thresh); % segment image
segmented_im(segmented_im == 1) = 0; % make background black (0)
segmented_im(segmented_im == 2) = 255; % make foreground white (255)
binary_im = im2bw(segmented_im); % make binary (logical) image
imshow(binary_im); % show binary image
binary_im il a logical matrix with false (0) for background, and true (1) for foreground. segmented_im is a double matrix with 0 for background and 255 for foreground. I hope this serves your purposes!
I'm trying to segment an image with Color-Based Segmentation Using K-Means Clustering. I already created 3 clusters, and the cluster number 3 is like this image:
This cluster has 3 different colors. And I want to only display the black spots of this image. How can I do that?
The image is 500x500x3 uint8.
Those "holes" look like they are well defined with the RGB values all being set to 0. To make things easy, convert the image to grayscale, then threshold the image so that any intensities less than 5 set the output to white. I use a threshold of 5 instead to ensure that we capture object pixels in their entirety taking variations into account.
Once that's done, you can use the function bwlabel from the image processing toolbox (I'm assuming you have it as you're dealing with images) where the second output tells you how many distinct white objects there are.
Something like this could work:
im = imread('http://i.stack.imgur.com/buW8C.png');
im_gray = rgb2gray(im);
holes = im_gray < 5;
[~,count] = bwlabel(holes);
I read in the image directly from StackOverflow, convert the image to grayscale, then determine a binary mask where any intensity that is less than 5, set the output to white or true. Once we have this image, we can use bwlabel's second output to determine how many objects there are.
I get this:
>> count
count =
78
As an illustration, if we show the image where the holes appear, I get this:
>> imshow(holes);
The amount of "holes" is a bit misleading though. If you specifically take a look at the bottom right of the image, there are some noisy pixels that don't belong to any of the "holes" so we should probably filter that out. As such, a simple morphological opening filter with a suitable sized structure will help remove spurious noisy islands. As such, use imopen combined with strel to define the structuring element (I'll choose a square) as well as a suitable size of the structuring element. After, use the structuring element and filter the resulting image and you can use this image to count the number of objects.
Something like this:
%// Code the same as before
im = imread('http://i.stack.imgur.com/buW8C.png');
im_gray = rgb2gray(im);
holes = im_gray < 5;
%// Further processing
se = strel('square', 5);
holes_process = imopen(holes, se);
%// Back to where we started
[~,count] = bwlabel(holes_process);
We get the following count of objects:
>> count
count =
62
This seems a bit more realistic. I get this image now instead:
>> imshow(holes_process);
Let's say i have an image like that one:
After some quick messing around, i got a binary image of the axe, like that:
What is the easiest/fastest way to get the contour of that image using GNU/Octave?
In Octave you can use bwboundaries (but I will welcome patches that implement bwtraceboundaries)
octave:1> pkg load image;
octave:2> bw = logical (imread ("http://i.stack.imgur.com/BoQPe.jpg"));
octave:3> boundaries = bwboundaries (bw);
octave:4> boundaries = cell2mat (boundaries);
octave:5> imshow (bw);
octave:6> hold on
octave:7> plot (boundaries(:,2), boundaries(:,1), '.g');
There are a couple of differences here from #Benoit_11 answer:
here we get the boundaries for all the objects in the image. bwboundaries will also accept coordinates as input argument to pick only a single object but I believe that work should be done by further processing your mask (may be due to the jpeg artifacts)
because we get boundaries for all objects, so you get a cell array with the coordinates. This is why we are using dots to plot the boundaries (the default is lines and it will be all over the image as it jumps from one object to other). Also, it is not documented whether the coordinates given are for the continuous boundary, so you should not assume it (again, why we plot dots).
the image that is read seems to have some artifacts, I will guess that is from saving in jpeg.
You can use bwtraceboundary in the Image package. Here is the Matlab implementation but that should be pretty similar using Octave:
First estimate starting pixel to look for boundary and then plot (BW is the image). (Check here )
dim = size(BW);
col = round(dim(2)/2)-90;
row = min(find(BW(:,col)));
boundary = bwtraceboundary(BW,[row, col],'N');
imshow(BW)
hold on;
plot(boundary(:,2),boundary(:,1),'g','LineWidth',3);
Output:
I have two images and have to divide that image. Due to manual error the area in the image is not as same. How can i correct it automatically using matlab so as to perform divison pixel by pixel accurately?
Once you've read both image files into the variables, say A and B, assuming A contains the image of the size you actually want and B contains the image of the size you want to change, you could use:
[numrows numcols] = size(A);
A = imresize(A, [numrows numcols]);
If I have an image, how can I obtain the values of each pixel in that image using matlab
Thanks.
Images are matrices (2D if grayscale, 3D if colored) in MATLAB.
You can use x(i,j) to access a pixel at location (i,j) in a grayscale image.
If the image is colored, you can use x(i,j,:) to access the r, g, b values in a 3-vector, respectively. If you need individual channels, then, you can use x(i,j,1) for red for example.
You may read this page to learn more.
You can use reshape to extract all the pixel values of the image into a vector with pixel values:
frame = imread('picture.jpg');
frame_size = size(frame);
allpixels = reshape(frame, frame_size(1)*frame_size(2), frame_size(3))
This can be useful when you want to vectorize your Matlab code (to avoid a for loop that goes through every pixel). To get back the original image representation:
frame2 = reshape(allpixels, frame_size);
to get the values at pixel(1,1) we simply write image(1,1).