I have written some lines of code using the following function:
adaptivethreshold(IM,ws,c)
and it gives me a Mask bw. I multiply this mask with my original image bb and show the result.
clear
clc
bb=dicomread('30421573');
figure(1)
imagesc(bb)
bw=adaptivethreshold(bb,50,128);
imaa=double(bw).*double(bb);
figure(2)
image(imaa)
the original Image and the result are shown:
It does not seem to be giving me any mask for my image. Is there any way I can extract those yellow parts from my results?
Try to create the mask before applying to the image, i.e.
bw=adaptivethreshold(bb,50,128);
BW = imbinarize(bb,bw);
imaa=double(bw).*double(BW);
figure(2)
image(imaa)
Related
Very new to MatLab, just figuring some things out and had a question. I am basically trying to filter/blur an image using conv2() but I am getting an all white image when I am using imshow()
I am reading the image in with
testImage = imread('test.bmp');
This is a uint8 image of a grayscale.
I am trying to convolve the image with a 4 x 4 matrix.
fourByFour = ones(4);
When I actually execute, I am getting all white with imshow()
convolvedImage = conv2(testImage, fourByFour);
I should expect a filter placed on the image, not an entirely white one.
Any help would be appreciated.
I don't have your test image so I explain on an image. As the definition of conv2 it returns the two-dimensional convolution.
So please look at this little code:
clc;% clear the screen
clear all;% clear everything
close all;% close all figures
test = imread('test.bmp');
% read test image that is .bmp format and size :294x294x3 and uint8.
fourByFour = ones(4); % define 4 * 4 matrix all ones
convolvedImage = conv2(test(:,:,1), fourByFour);
%apply the ones matrix on image but to use conv2 on this image we apply on one of channels
figure, imshow(convolvedImage,[])
This is my command window, out put:
I'm using MAtlab 2017a, and if I use conv2(test, fourByFour); instead of conv2(test(:,:,1), fourByFour); ,the error is :
Error using conv2
N-D arrays are not supported.
So we should attention to class type and dimensions. And one more thing, in your command window please type edit conv2 you can read the details of this function and how to use it, but never edit it:). Thanks
test = imread('test.bmp');
% read test image that is .bmp format and size :294x294x3 and uint8.
fourByFour = ones(4);
% define 4 * 4 matrix all ones
test=rgb2gray(test);
% convert colorimage into gray
convolvedImage = conv2(double(test), double(fourByFour));
%perform the convolution
figure, imshow(convolvedImage,[])
% display the image
I have an image in gray scale and I would like to remove the edge that is extracted by using an edge extraction method in MATLAB.
Is it possible to do it?
Below is my code that perform the edge extraction ：
%load the image
A=imread('MikuBW.jpg');
%predefined edge extraction method in MATLAB
g=edge(A,'canny');
%plot results
subplot(2,1,1);
imshow(A),title('ORIGINAL');
subplot(2,1,2);
imshow(g),title('CANNY');
!(http://i.imgur.com/uS2Xxwf.jpg) [the result of original image and after edge extraction][1]
how do I remove the edge that is extracted in "CANNY" from the original image?
thank you for any help possible! =)
if you just want the "Canny" part to be removed, i.e. to be replaced with zeros, you can simply write:
A(g) = 0;
however, a more "natural" approach will be to blur the image, so it will have less edges in the first place:
H = fspecial('disk',5);
A = imfilter(A,H,'replicate');
So lets say I have black & white image that is read with imread() command and saved into matrix A.
I want to output/graph this matrix A image in a cylinder shape. I know how to draw a cylinder in MATLAB, but I do not have a clue what I should do if I want to put image on a cylinder or draw image in cylinder shape. Any help will be appreciated. Thank you.
I found this site from googling.
http://www.flashandmath.com/advanced/rolls/cylin.html
This is exactly what I want to do, but I need to do this in MATLAB.
The technique is called texture mapping. This is a code example from surface function (R2011b):
load clown
surface(peaks,flipud(X),...
'FaceColor','texturemap',...
'EdgeColor','none',...
'CDataMapping','direct')
colormap(map)
view(-35,45)
This example loads RGB image from "peppers.png" and maps it onto cylinder:
imgRGB = imread('peppers.png');
[imgInd,map] = rgb2ind(imgRGB,256);
[imgIndRows,imgIndCols] = size(imgInd);
[X,Y,Z] = cylinder(imgIndRows,imgIndCols);
surface(X,Y,Z,flipud(imgInd),...
'FaceColor','texturemap',...
'EdgeColor','none',...
'CDataMapping','direct')
colormap(map)
view(-35,45)
Things are even simpler with the warp function (comes with Image Processing toolbox) as natan suggested:
imgRGB = imread('peppers.png');
[imgRows,imgCols,imgPlanes] = size(imgRGB);
[X,Y,Z] = cylinder(imgRows,imgCols);
warp(X,Y,Z,imgRGB);
In my progress work, I have to detect a parasite. I have found the parasite using HSV and later made it into a grey image. Now I have done edge detection too. I need some code which tells MATLAB to find the largest contour (parasite) and make the rest of the area as black pixels.
You can select the "largest" contour by filling in the holes that each contour surrounds, figure out which shape gives you the largest area, then use the locations of the largest area and copy that over to a final image. As what Benoit_11 suggested, use regionprops - specifically the Area and PixelList flags. Something like this:
im = imclearborder(im2bw(imread('http://i.stack.imgur.com/a5Yi7.jpg')));
im_fill = imfill(im, 'holes');
s = regionprops(im_fill, 'Area', 'PixelList');
[~,ind] = max([s.Area]);
pix = sub2ind(size(im), s(ind).PixelList(:,2), s(ind).PixelList(:,1));
out = zeros(size(im));
out(pix) = im(pix);
imshow(out);
The first line of code reads in your image from StackOverflow directly. The image is also a RGB image for some reason, and so I convert this into binary through im2bw. There is also a white border that surrounds the image. You most likely had this image open in a figure and saved the image from the figure. I got rid of this by using imclearborder to remove the white border.
Next, we need to fill in the areas that the contour surround, so use imfill with the holes flag. Next, use regionprops to analyze the different filled objects in the image - specifically the Area and which pixels belong to each object in the filled image. Once we obtain these attributes, find the filled contour that gives you the biggest area, then access the correct regionprops element, extract out the pixel locations that belong to the object, then use these and copy over the pixels to an output image and display the results.
We get:
Alternatively, you can use the Perimeter flag (as what Benoit_11) suggested, and simply find the maximum perimeter which will correspond to the largest contour. This should still give you what you want. As such, simply replace the Area flag with Perimeter in the third and fourth lines of code and you should still get the same results.
Since my answer was pretty much all written out I'll give it to you anyway, but the idea is similar to #rayryeng's answer.
Basically I use the Perimeter and PixelIdxList flags during the call to regionprops and therefore get the linear indices of the pixels forming the largest contour, once the image border has been removed using imclearborder.
Here is the code:
clc
clear
BW = imclearborder(im2bw(imread('http://i.stack.imgur.com/a5Yi7.jpg')));
S= regionprops(BW, 'Perimeter','PixelIdxList');
[~,idx] = max([S.Perimeter]);
Indices = S(idx).PixelIdxList;
NewIm = false(size(BW));
NewIm(Indices) = 1;
imshow(NewIm)
And the output:
As you see there are many ways to achieve the same result haha.
This could be one approach -
%// Read in image as binary
im = im2bw(imread('http://i.stack.imgur.com/a5Yi7.jpg'));
im = im(40:320,90:375); %// clear out the whitish border you have
figure, imshow(im), title('Original image')
%// Fill all contours to get us filled blobs and then select the biggest one
outer_blob = imfill(im,'holes');
figure, imshow(outer_blob), title('Filled Blobs')
%// Select the biggest blob that will correspond to the biggest contour
outer_blob = biggest_blob(outer_blob);
%// Get the biggest contour from the biggest filled blob
out = outer_blob & im;
figure, imshow(out), title('Final output: Biggest Contour')
The function biggest_blob that is based on bsxfun is an alternative to what other answers posted here perform with regionprops. From my experience, I have found out this bsxfun based technique to be faster than regionprops. Here are few benchmarks comparing these two techniques for runtime performances on one of my previous answers.
Associated function -
function out = biggest_blob(BW)
%// Find and labels blobs in the binary image BW
[L, num] = bwlabel(BW, 8);
%// Count of pixels in each blob, basically should give area of each blob
counts = sum(bsxfun(#eq,L(:),1:num));
%// Get the label(ind) cooresponding to blob with the maximum area
%// which would be the biggest blob
[~,ind] = max(counts);
%// Get only the logical mask of the biggest blob by comparing all labels
%// to the label(ind) of the biggest blob
out = (L==ind);
return;
Debug images -
i'm trying to perform mean filtering on an image in Matlab, i have to make it modular so i have another function for my averaging and then my script calls the function. I run the script and there are no errors but it doesn't seem to do the filtering as the output of the image is no different to the original. Can anyone see where i am going wrong?
%input image
image1 = imread('moon.jpg');
%convert to grayscale
%mean filtering
mean = averagefilter2(image1);
image_grey = rgb2gray(mean);
figure;
imshow(image_grey);
%my average filter function%
function img=averagefilter2(image1)
meanFilter = fspecial('average',[3 3]);
img = imfilter (image1,meanFilter);
end
Thanks!
Can you show your figures of image1 and image_grey? You can also try to imagesc(abs(image1-image_grey)) to see the difference between the original image and the averaged one. I ran your code and didn't see problems. I can observed the smooth effect in my sample image.
Remember to apply rgb2gray to your image1 as well.