Spheroids detction from Images

Question

I need to find the blobs from the below image. The major problem is background. background doesn't have uniform intensity. I tried couple of things like thresholding and edge detection in MATLAB but couldn't able to find a better ways to segment out all spheroids. I need to extract the blobs and I need to find the area of each blobs. Does anyone know how to work-around with this kind of background?

Edit (07/02/17): As suggested by Spektre I tried Following things in MATLAB.

Method 1:

img_original = imread('~/my_image.jpg'); %Read image
img_ch = single(img_original(:,:,2));    %Pick one channel( here its green)

g = fspecial('gaussian',200,100);        %Kernel matrix to make the img blurr
con_img = conv2(img_ch,g,'same');        %2D convolution, this wil make the img blurr

sub_img = (con_img - img_ch);            %Simple matrix subtraction

sub_img(sub_img <= 10) = 0;              %Thresholding
sub_img(sub_img ~= 0) = 1;

fil_sub = imfill(sub_img,'holes');       %Fill the regions
imgfilt = imfilter(fil_sub, ones(3));    %run filter using 3by3 matrx
imgfilt(imgfilt < 8) = 0;                %Reduce noisy pixels by thresholding
mfilt_img = (medfilt2(imgfilt));         %reduce unwanted pixels

img = img_ch;
img(mfilt_img ~= 0) = 255;
img2 = img_ch;
img2(img2 < 70) = 0;                     %Threshold for darker pixels which are left out from above methode.
img(img2 ==0) = 255;
disp_img = img_original(:,:,1);
disp_img(img ==255) = 255;
img_original(:,:,1) = disp_img;
figure, imshow(img_original)

I got the segments but still not good enough I think. This method gave good segments in the high intensity background, Even if I reduce the threshold value segments are not clear in the darker background and brightest pixels in the blobs are excluded.

Method 2:

img_original = imread('~/cancer_cells/Snap-10234.jpg'); %Read image
img_ch = single(img_original(:,:,2));    %Pick one channel( here its green)
clear new_matcel cur_img matcel avg_matrx

s=3;                % Set size of the square window               
mat_img = img_ch;   % Working image channel
% resize the working matrix so that the dimensions matches
resize_img = resizem(mat_img,round(size(mat_img)/s)*s);

% convert matrix into small s x s matrix and save each in cells
window_c = ones(1,size(resize_img,1)/s) * s;
window_r = ones(1,size(resize_img,2)/s) * s;
mat_cel = mat2cell(resize_img,window_c,window_r);
new_matcel = cell(size(mat_cel));   % initialize new variable

% get the average value for each window and replace the actual by avg value
for i = 1:size(mat_cel,1)
    for j = 1:size(mat_cel,2)
        cur_img = mat_cel{i,j};
        avg_value = mean(mean(cur_img));
        new_matcel{i,j} = ones(s) * avg_value;
    end
end

avg_matrx = cell2mat(new_matcel);       % convert cells to matrix 

image_sub = (abs(resize_img - avg_matrx)); % take the absolute difference
image_sub(image_sub < 7) = 0;           % thresholding
image_sub(image_sub~=0) = 1;

image_sub = bwmorph(image_sub,'bridge');% fill gaps
image_sub = imfill(image_sub,'holes'); % fill the bounded regioons
% image_sub(image_sub == 1) = 255;
image_sub = resizem(image_sub,size(img_ch)); % resize to original size

disp_img = img_original(:,:,1);
disp_img(image_sub == 1) = 255;
img_original(:,:,1) = disp_img;
figure, imshow(img_original)

Much better segmented image:

even brighter pixels are included in the segment. Thanks to Spektre.

Is there a way to improve the above code? or any other idea to get more precise segments?

Thanks.