So I modified an example on the MathWorks website to roughly accomplish your goal. Ultimately I get a bunch of cells whose measurements are stored in the output of 'regionprops', in the 'cellProps' array. From here you could get centroid locations and you can get the equivalent diameter as well to model a circle as you planned. Then you can use 'viscircles' to see them on your image.
I have some experience in microscope image processing as well, and I have a few suggestions. First, try to remove the reticle from your images. It messes up the dynamic range and makes it a bit harder to process the images. Also, try to correct the image so that the field is relatively flat across it. It can be a lot of work writing automation algorithms for microscopy image processing since a lot of cells are relatively amorphous and because you get a lot of image noise, vignetting/illumination gradients, and sometimes foreign particles. In your case, you have heavy cell occlusion in the center of the image, which isn't impossible to overcome but you need the right algorithm. There are some published online. A book I can recommend is 'Microscope Image Processing' by Wu, Merchant and Castleman. Anyways, here is the algorithm adapted from this example:
https://blogs.mathworks.com/steve/2006/06/02/cell-segmentation/
rgb = imread('1p40x-400ms-DS-Fi2-3.jpg');
I = rgb2hsv(rgb);
% Try using the Value channel of the HSV colorspace
Iv = I(:,:,3); %better than your grayscale img
% Kill the reticle bar
Iv(Iv>0.85) = mean2(Iv); % I wouldn't do this in every case but if you can, get rid of the reticles in your image, they throw off the dynamic range and make things weirder
se = strel('disk',50);
% Try getting rid of some of the haze with a top hat filter
tophatFiltered = imtophat(Iv,se);
figure
% Do a little prep work. Gaussian blur, adaptive histeq, and sharpen
I2 = imsharpen(adapthisteq(imfilter(tophatFiltered,fspecial('gaussian',9,3))));
% Auto threshold
bw = im2bw(I2, graythresh(I2));
imshow(bw)
bw2 = imfill(bw,'holes');
bw3 = imopen(bw2, ones(5,5));
bw4 = bwareaopen(bw3, 40);
bw4_perim = bwperim(bw4);
overlay1 = imoverlay(I2, bw4_perim, [.3 1 .3]);
imshow(overlay1)
mask_em = imextendedmax(I2, 35/255);
figure
imshow(mask_em)
figure
mask_em = imclose(mask_em, ones(5,5));
mask_em = imfill(mask_em, 'holes');
mask_em = bwareaopen(mask_em, 40);
overlay2 = imoverlay(I2, bw4_perim | mask_em, [.3 1 .3]);
imshow(overlay2)
I_eq_c = imcomplement(I2);
I_mod = imimposemin(I_eq_c, ~bw4 | mask_em);
L = watershed(I_mod);
imshow(label2rgb(L))
% Use the label matrix for region definition, and Iv for intensity measurements
cellProps = regionprops(L,Iv,'all');
