separating foreground from background and working on the masked image in matlab

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Malini Bakthavatchalam 2020-9-24

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https://ww2.mathworks.cn/matlabcentral/answers/599452-separating-foreground-from-background-and-working-on-the-masked-image-in-matlab

编辑： Sindar 2020-10-2

Hi,

This question is haunting me for a long time. first I was working in color so I have asked people here. now i am working on grey levels, but still this question is not resolved. I am reading an image using imread function then converting the whole image to grey levels, then I have to work only on the grey levels of the foreground, the code should not include the background, could anyone suggest a code to insert in my code please....i will attach a sample image as well ...

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Sindar 2020-9-25

在 MATLAB Online 中打开

everything above "figure(1)" can be replaced by this cleaner version

% image included in Matlab so we can compare
I = imread('ngc6543a.jpg');
G = rgb2gray(I);
my_median = median(G,"all");
K = my_median*ones(size(G),"uint8");
P = my_median*ones(size(G),"uint8");
idx = ( G <= my_median );
K(idx) = G(idx);
P(~idx) = G(~idx);

if you have a mask as a size(G) set of true/false in variable BW (the first output from image Segmenter app auto-generated code), then replace the my_median line with:

my_median = median(G(mask));

With the sample image and a segmenting function below, I get this:

[BW,G_masked] = segmentImage(G);

G_masked

BW (i.e. the mask)

Your plots

Segmenter code

function [BW,maskedImage] = segmentImage(X)
%segmentImage Segment image using auto-generated code from imageSegmenter app
%  [BW,MASKEDIMAGE] = segmentImage(X) segments image X using auto-generated
%  code from the imageSegmenter app. The final segmentation is returned in
%  BW, and a masked image is returned in MASKEDIMAGE.
% Auto-generated by imageSegmenter app on 24-Sep-2020
%----------------------------------------------------
% Threshold image - manual threshold
BW = X > 25;
% Close mask with disk
radius = 20;
decomposition = 0;
se = strel('disk', radius, decomposition);
BW = imclose(BW, se);
% Clear borders
BW = imclearborder(BW);
% Erode mask with disk
radius = 1;
decomposition = 0;
se = strel('disk', radius, decomposition);
BW = imerode(BW, se);
% Create masked image.
maskedImage = X;
maskedImage(~BW) = 0;
end

Sindar 2020-9-26

在 MATLAB Online 中打开

First, the code:

It seems mostly like the algorithm is correct, but many lines refer to variables that aren't defined. Here's a functional version (minus the image segmenter):

clc 
close all
clear all
I = imread('img29res.jpg');
G = rgb2gray(I);
[BW,G_masked] = segmentImage(G);
% change to true if you want to use the median of the whole image
if false
    my_median = median(G,"all");
    K = my_median*ones(size(G),"uint8");
    P = my_median*ones(size(G),"uint8");
    idx = ( G <= my_median );
    K(idx) = G(idx);
    P(~idx) = G(~idx);
else
    my_median = median(G(BW));
    K = my_median*ones(size(G),"uint8");
    P = my_median*ones(size(G),"uint8");
    idx_dark = ( G <= my_median ) | ~BW;
    K(idx_dark) = G(idx_dark);
    idx_light = ( G > my_median ) | ~BW;
    P(idx_light) = G(idx_light);
end
figure(1)
subplot(3,3,1)
imshow(I)
title('original');
subplot(3,3,2)
imshow(G)
title('grayscale');
subplot(3,3,3)
imshow(BW)
title('foreground mask');
subplot(3,3,4)
imshow(G_masked)
title('orginal foreground');
subplot(3,3,5)
imshow(K)
title('lower rectified');
subplot(3,3,6)
imshow(P)
title('upper rectified');
subplot(3,3,7)
imhist(G(BW))
title('originalhist');
% get the limits of the orginal histogram and use the same for others
yl=ylim();
subplot(3,3,8)
imhist(K(BW))
title('lower rectified hist');
ylim(yl)
subplot(3,3,9)
imhist(P(BW))
title('upper rectified hist');
ylim(yl)

I added a row of plots to show the original color image, the mask itself, and just the foreground of the grayscale image.

Now, to the background/foreground segmentation:

The idea of the app is that you can play around with options to figure out what works for a few sample images. You generate code, paste it as "function [BW,maskedImage] = segmentImage(X)...". Then, you try it out on the whole batch and see if there are issues. So, no, you don't need to run the app for each image. If all your images have a solid light background, it should be relatively easy.

Right now, segmentation code you're using (mine?) doesn't work for your image:

You can see that it identifies none of the image as foreground

I just downloaded your actual image, played around with the App, and came up with this code. It works for this particular image, but you'll need to check and adjust it so it works decently for all images:

function [BW,maskedImage] = segmentImage(X)
%segmentImage Segment image using auto-generated code from imageSegmenter app
%  [BW,MASKEDIMAGE] = segmentImage(X) segments image X using auto-generated
%  code from the imageSegmenter app. The final segmentation is returned in
%  BW, and a masked image is returned in MASKEDIMAGE.
% Auto-generated by imageSegmenter app on 26-Sep-2020
%----------------------------------------------------
% Adjust data to span data range.
X = imadjust(X);
% Threshold image - adaptive threshold
BW = imbinarize(X, 'adaptive', 'Sensitivity', 0.630000, 'ForegroundPolarity', 'bright');
% Invert mask
BW = imcomplement(BW);
% Fill holes
BW = imfill(BW, 'holes');
% Close mask with disk
radius = 41;
decomposition = 0;
se = strel('disk', radius, decomposition);
BW = imclose(BW, se);
% Create masked image.
maskedImage = X;
maskedImage(~BW) = 0;
end

My suggestion would be to convert the script into a function that accepts an image, then you can run it on all of them quickly (there's even an Image Batch Processor App, but I have no experience with it)

Malini Bakthavatchalam 2020-10-2

Thank you for the reply. I understand the concept you explained for the background removal. its working in my code, but if I have images with background other than whites i cannot use this alogrithm right ?

like i am attaching a sample image

Sindar 2020-10-2

编辑：Sindar 2020-10-2

This segmenter algorithm will likely fail, but you may be able to build a new algorithm in the Image Segmenter app (or Color Thresholder app). It really depends on the diversity of your images. For example, I'd expect 6.jpg to be almost trivial to segment, whereas 10.jpg and 25.jpg are probably doable but trickier, and creating a single algorithm that works on all three could be difficult.

(everything else in the code will function the same)

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