I have a image with small objects. I have calculated their areas using
area=regionprops(CC,'Area');
CC is the connected components returned by
CC=bwconncomp(BW);
Now i need to remove objects with area less than 15 (set them to zero in the original image BW).
I know i can do this with a more simpler ways other than that from regionprops, but I need to do this from the output of regionprops, because I will extract other properties that is supported by regionprops and filter again the image according to these extracted features in a similar way. Can anyone help me with this task?
After the two commands you show,
CC=bwconncomp(BW);
area=regionprops(CC,'Area');
area is a struct array where area(ii).Area is the area for object ii. This corresponds to the connected component given by CC.PixelIdxList{ii}.
You can find the indices with a small area by
I = find([area.Area] < 15);
Then,
CC.PixelIdxList{I}
gives a comma-separated list of vectors with pixel indices. You can join these vectors into a single vector using cat:
pixels = cat(1,CC.PixelIdxList{I});
Now all that is left is setting those pixels to 0 in the input image:
BW(pixels) = 0;
Related
In the following image, all the rectangles are a little bit deformed due to power leakage effect. I want to detect all the rectangles and obtain the positions of the rectangles. If the number of rectangles are arbitrary (maybe unknown), how to detect?
Is there a way to detect all rectangles with MATLAB?
The rectangles are against a black background so the easiest way to likely do this is to consider the image as a simple 2D array with values from 0 to whatever (assuming 0 is black).
You can then write and statement that says while array value is above 0 output the array coords in x and y to a new array. Once you have done this you can then write another script to find the corners of the boxes by looking to see whether each coordinate could be equal to another coordinate if you added or subtracted 1, this would give you the number of boxes and the coordinates in each box.
Then you should be able to find the centre from there.
You need to perform threshold of the image with some level. And perform regionprops(). Read the MATLAB help Documentation.
Also refer the MATLAB code below.
clc
MainImg = imread('ifVsy.jpg');
BinImg = im2bw(MainImg,graythresh(MainImg));
Objects = regionprops(BinImg,'centroid');
AllCenters = cat(1, Objects.Centroid);
imshow(MainImg);
hold on
plot(AllCenters(:,1),AllCenters(:,2),'r*');
hold off
I have a segmented image. I wish to extract the middle pixel(s) of each segmentation. The goal is to extract the mean color from the middle pixel.
The following diagram illustrates what I mean by 'middle pixel':
The alternative middle pixels are also acceptable.
What algorithms/functions are available in Matlab to achieve something similar to this? Thanks.
If I'm understanding what you want correctly, you're looking for the centroid. MATLAB has the regionprops function which measures the properties of separate binary objects as long as the objects.
You can use the Centroid property. Assuming your image is stored in im and is binary, something like this will do:
out = regionprops(im, 'Centroid');
The output will be a structure array of N elements where N corresponds to the total number of objects found in the image. To access the ith object's centroid, simply do:
cen = out(i).Centroid;
If you wish to collect all centroids and place them perhaps in a N x 2 numeric array, something like this would work:
out = reshape([out.Centroid], 2, []).';
Each row would be the centroid of an object found in the image. Take note that an object is considered to be a blob of white pixels that are connected to each other.
I have a segmented image. i need to verify the intensity variation of the ellipse like structure present in the image. I need to check whether that ellipse is homogeneously white
original image
ellipse like structure is inside the rectangle
my segmented image is
i want to compare the original image (which is homogeneous white) with the segmented region.
regionprops is perfect for this sort of task. You can pass it your segmented binary image, and your original image to retrieve a list of the pixels in each region (presuming each region is not connected, as shown in your sample image). These will be in the form of a n x 1 vector for each region, returned as a struct array.
stats = regionprops(BW, I, 'PixelValues');
(You may want to retrieve other values returned by regionprops, like BoundingBox or Centroid, to help identify which set of pixels belongs to which region more easily. Consult the documentation to see what options are available.).
You can then define some statistical function to show the variation within each region, for example, to calculate the variance and standard deviation for each:
for n = 1:length(stats)
stats(n).var = var(stats(n).PixelValues);
stats(n).std = std(stats(n).PixelValues);
end
If you have some other specific definition of "intensity variation" in mind, then you need to develop some function that calculates it, then just call that instead of a built in like var or std.
I have an image that was read in using the imread function. My goal is to collect pairs of pixels in an image in MATLAB. Specifically, I have read a paper, and I am trying to recreate the following scenario:
First, the original image is grouped into pairs of pixel values. A pair consists of two neighboring pixel values or two with a small difference value. The pairing could be done horizontally by pairing the pixels on the same row and consecutive columns, or vertically, or by a key-based specific pattern. The pairing could be through all pixels of the image or just a portion of it.
I am looking to recreate the horizontal pairing scenario. I'm not quite sure how I would do this in MATLAB.
Assuming your image is grayscale, we can easily generate a 2D grid of co-ordinates using ndgrid. We can use these to create one grid, then shift the horizontal co-ordinates to the right to make another grid and then use sub2ind to convert the 2D grid into linear indices. We can finally use these linear indices to create our pixel pairings that you have described in your comments (you should really add that to your post BTW). What's important is that you need to skip over every other column in a row to ensure unique pixel pairings.
I'm also going to assume that your image is grayscale. If we go to colour, this will be slightly more complicated, and I'll leave that to you as a learning exercise. Therefore, assuming your image was read in through imread and is stored in im, do something like this:
[rows,cols] = size(im);
[X,Y] = ndgrid(1:rows,1:2:cols);
ind = sub2ind(size(im), X, Y);
ind_shift = sub2ind(size(im), X, Y+1);
pixels1 = im(ind);
pixels2 = im(ind_shift);
pixels = [pixels1(:) pixels2(:)];
pixels will be a 2D array, where each row gives you the pixel intensities of a particular pairing in the image. Bear in mind that I processed each row independently. As such, as soon as we are done with one row, we simply move on to the next row and continue the procedure. This also assumes that your image has an even number of columns. Should it not, you have a decision to make. You need to either pad the image with one column at the end, and this column can be anything you want, or you can remove this column from the image before processing. If you want to fill in this column, you can either make it all zeroes, or perhaps replicate the last column and place this beside the last column in the original image. Therefore, an appropriate pre-processing step may look something like this:
if mod(cols,2) ~= 0
im = im(:,1:end-1);
end
The above code simply removes the last column in the image if the number of columns is odd. Once you run through this code, you can run the first bit of code that I had above.
Good luck!
I have a volume (3D matrix) that has undergone a segmentation process. Most of the volume consist of NaNs (or zeros), except regions that have passed some criteria (see picture). I need to know how large each remaining segment is in number of voxels and how is their distribution on the 2D planes (xy, xz, yz). Is there anything in matlab that can help me do this in an efficient way rather than direct search? The volume can be rather large. For ex. in the attached picture there is one segment in yellowish/brownish colour of 7 voxels and extends more vertically than in xy.
Thanks in advance.
The most convenient solution is to use REGIONPROPS. In your example:
stats = regionprops(image, 'area', 'centroid')
For every feature, there is an entry in the structure stats with the area (i.e. # of voxels) and the centroid.
I think that what you are looking for is called bwlabeln. It allows you to find blobs in 3D space, just like bwlabel does in 2D. Afterwards, you can use regionprops to find out the properties of the data.
Taken directly from help:
bwlabeln Label connected components in binary image.
L = bwlabeln(BW) returns a label matrix, L, containing labels for the
connected components in BW. BW can have any dimension; L is the same
size as BW. The elements of L are integer values greater than or equal
to 0. The pixels labeled 0 are the background. The pixels labeled 1
make up one object, the pixels labeled 2 make up a second object, and
so on. The default connectivity is 8 for two dimensions, 26 for three
dimensions, and CONNDEF(NDIMS(BW),'maximal') for higher dimensions.