I have an MRI volumetric array and a probability map on top of it which maps from 0 to 1.
I'd like to have the MRI shown as slices (one for each plane) in black and white, and the probability map as a volume over it (ray cast) on a scale from blue, 0 opacity to red full opacity.
For now I can't even overlay the two volumes using different colormaps. How do I do it?
I would comment because I don't believe this is an answer but I don't have the reputation to comment.
For different colormaps you need different scalar attributes names.
If they have the same name they will be colored by the same colormap.
If this is not helpfull, let us know more details of your datasets so we can help you better!
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im trying to identify a specific shade of green leaves (e.g. navy green) from the attached image. how do i do that in the most efficient way? So far, i'm converting the RGB to HSV and then thresholding the image based on some specific range of saturation and value that will isolate my desired shade. it's working on some images and it's just all over the place on others. i want something that can isolate a specific shade of green in any different image that has slightly different saturation and value (e.g. if the picture was taken with too much light)
Image link
pic=imread('image.jpg');
q=rgb2hsv(pic);
H=q(:,:,1);
S=q(:,:,2);
V=q(:,:,3);
thresh=S>0.6111 & S<0.6666 & V>0.3888 & V<0.4583;
st=strel('diamond',20);
w=imdilate(thresh,st);
comps=bwconncomp(w,8);
num=comps.NumObjects;
fprintf('The number of leaves is %i',num)
% then i try to have some pointers on the image to show me where matlab has identified the the shade.
m = regionprops(w,'centroid');
boxes = cat(1, m.Centroid);
imshow(pic)
hold on
plot(boxes(:,1),boxes(:,2), 'b*')
hold off
Your help will be highly appreciated.
Either the HSV color space (hey, S is saturation and V value), where H will give you the hue,or CIE-Lab color space, where euclidean distance will give you how close 2 specific pixel are to each other in color.
This answer explains how to do it for HSV: Segment pixels in an image based on colour (Matlab)
Using combined with CIE-LAB may help if the colors are very close together (like the greens in each leaf), but you should give HSV a shot
I'm learning about statistical feature of an image.A quote that I'm reading is
For the first method which is statistical features of texture, after
the image is loaded, it is converted to gray scale image. Then the
background is subtracted from the original image. This is done by
subtract the any blue intensity pixels for the image. Finally, the ROI
is obtained by finding the pixels which are not zero value.
The implementation :
% PREPROCESSING segments the Region of Interest (ROI) for
% statistical features extraction.
% Convert RGB image to grayscale image
g=rgb2gray(I);
% Obtain blue layer from original image
b=I(:,:,3);
% Subtract blue background from grayscale image
r=g-b;
% Find the ROI by finding non-zero pixels.
x=find(r~=0);
f=g(x);
My interpretation :
The purpose of substracting the blue channel here is related to the fact that the ROI is non blue background? Like :
But in the real world imaging like for example an object but surrounded with more than one colors? What is the best way to extract ROI in that case?
like for example (assuming only 2 colors on all parts of the bird which are green and black, & geometri shaped is ignored):
what would I do in that case? Also the picture will be transformed to gray scale right? while there's a black part of the ROI (bird) itself.
I mean in the bird case how can I extract only green & black parts? and remove the rest colors (which are considered as background ) of it?
Background removal in an image is a large and potentielly complicated subject in a general case but what I understand is that you want to take advantage of a color information that you already have about your background (correct me if I'm wrong).
If you know the colour to remove, you can for instance:
switch from RGB to Lab color space (Wiki link).
after converting your image, compute the Euclidean from the background color (say orange), to all the pixels in your image
define a threshold under which the pixels are background
In other words, if coordinates of a pixel in Lab are close to orange coordinates in Lab, this pixel is background. The advantage of using Lab is that Euclidean distance between points relates to human perception of colours.
I think this should work, please give it a shot or let me know if I misunderstood the question.
I'm trying to segment the sky and water part in this image.
Link of the Picture
I've tried so many methods like k-means, threshold, multi threshold etc. BUt unfortunately nothing worked so well.
Here is an example of my code(Matlab):
img=imread('1.jpg');
im_gray=rgb2gray(img);
b=imadjust(im_gray);
imshow(b);
bw_remove_small=imopen(b,strel('square',5));
imshow(bw_remove_small); %after 1st iteration
m3=medfilt2(bw_remove_small,[18,16]);
imshow(m3);
m3=medfilt2(bw_remove_small,20,20]);
m3=medfilt2(bw_remove_small,[20,20]);
imshow(m3);
I1=m3;
I2=rgb2gray(I1);
I=double(I2);
figure
subplot(1,3,1)
imshow(I1)
subplot(1,3,2)
imshow(I2)
g=kmeans(I(:),4);
J = reshape(g,size(I));
subplot(1,3,3)
imshow(J,[]);
Can any one help me?please
The picture's two regions are different in hue, texture, and gray level brightness.
The horizon is the best line in the image from our point of view and can be seen by the distinct change in brightness. The brightness will not work with a single threshold because the image brightness is not flat, so use brightness a model of the distribution to flatten out the sky or the water. This implies knowledge of the objective but there are two things that can give you an approximate answer: texture and/or hue.
The hue with a threshold of 120 (derived from the hue histogram) will give you the two regions but will not be divided cleanly and will have overlapping sections. Though using these two sections a model of the brightness can be found.
The same with texture. Using a small fft of the image, subtracting the dc out, then averaging or just summing up the non dc parts will result in a histogram with two peaks that may not be as distinct as the hue's is but is enough to find a threshold and two areas that will allow a model of the brightness to be found.
The key fact is if the sky is modeled properly as a gray surface then you can subtract it out of the image and use a simple threshold to pull it out.
Edge detection is very noisy in this image to be able to easily see the line but if you can pull out the image lines without losing shape then look for a straight and long contour it may take less code/work.
Hope this helps some! I used this to find mountains in the distance when there was not a big difference between the sky and the mountains. Plus I just tried this on your pic and almost got a good answer without a good model of the sky.
I want to count number of Objects in an Image identify by each color.
Example: I want to count yellow rices or green rices in the image.
Would you please give me some tip to do it?
You've taken this image from the rice demonstration that ships with MATLAB's Image Processing Toolbox. If you read through the demo, you already have some code that will isolate each rice grain individually - that's how the image was created.
As for counting the number of grains that have a particular color such as yellow or green, again read through the demo: it's clear that the rice grains are not colored with a small set of discrete colors such as yellow or green (or orange or pink). Rather, the rice grain colors have been specifically created so that they are equally spaced throughout the colormap spring.
So to proceed with your task you're going to have to provide some definition of 'yellow' or 'green', perhaps in terms of being within a particular range of RGB values.
Having done this, you can then use the variable labeled (that is constructed for you in the demo) together with the regionprops command, to give you a list of the pixels that are within each rice grain boundary. Simply compare those pixels to your definitions of yellow or green, and you're there.
assuming you have an image in matrix a (sized m*n*3), and you want to find the number of objects with color [r,g,b].
first, select only pixels with the correct color:
bb = (a(:,:,1) == r & a(:,:,2) == g &a(:,:,3) == b);
Than:
[~,num] = bwlabel(bb,8)
num is the number of objects.
This is best done by working in a different colorspace than the RGB one (think HSV, Lab*, ..).
Steve Eddins posted a series of articles showing an example how to segment objects of certain color from an image (green M&Ms in his case):
What color is green?
Two-Dimensional Histograms
a* and b*
Freehand segmentation in the a*-b* plane
More on segmenting in a*-b* space
we are doing a mat lab based robotics project.which actually sorts objects based on its color so we need an algorithm to detect specific color from the image captured from a camera using mat lab.
it will be a great help if some one can help me with it.its the video of the project
In response to Amro's answer:
The five squares above all have the same Hue value in HSV space. Selecting by Hue is helpful, but you'll want to impose some constraints on Saturation and value as well.
HSV allows you to describe color in a more human-meaningful way, but you still need to look at all three values.
As a starting point, I would use the rgb space and the euclidian norm to detect if a pixel has a given color. Typically, you have 3 values for a pixel: [red green blue]. You also have also 3 values defining a target color: [255 0 0] for red. Compute the euclidian norm between those two vectors, and apply a decision threshold to classify the color of your pixel.
Eventually, you want to get rid of the luminance factor (i.e is it a bright red or a dark red?). You can switch to HSV space and use the same norm on the H value. Or you can use [red/green blue/green] vectors. Before that, apply a low pass filter to the images because divisions (also present in the hsv2rgb transform) tend to increase noise.
You probably want to convert to the HSV colorspace, and detect colors based on the Hue values. MATLAB offers the RGB2HSV function.
Here is an example submission on File Exchange that illustrate color detection based on hue.
For obtaining a single color mask, first of all convert the rgb image gray using rgb2gray. Also extract the desired color plane from the rgb image ,(eg for obtaining red plain give rgb_img(:,:,1)). Subtract the given plane from the gray image........