I came up with the following program to segment the regions and hopefully locate the pattern of interest using template matching. I've added some comments and figure titles to explain the flow and some resulting images. Hope it helps.
im = imread('sample.png');
gr = rgb2gray(im);
bw = im2bw(gr, graythresh(gr));
bwsm = imresize(bw, .5);
dism = bwdist(bwsm);
dismnorm = dism/max(dism(:));
figure, imshow(dismnorm, []), title('distance transformed')
eq = histeq(dismnorm);
eqcl = imclose(eq, ones(5));
figure, imshow(eqcl, []), title('histogram equalized and closed')
eqclbw = eqcl < .2; % .2 worked for samples given
eqclbwcl = imclose(eqclbw, ones(5));
figure, imshow(eqclbwcl, []), title('binarized and closed')
filled = imfill(eqclbwcl, 'holes');
figure, imshow(filled, []), title('holes filled')
% -------------------------------------------------
% template
tmpl = zeros(16);
tmpl(3:4, 2:6) = 1;tmpl(11:15, 13:14) = 1;
tmpl(3:10, 7:14) = 1;
st = regionprops(tmpl, 'orientation');
tmplAngle = st.Orientation;
% -------------------------------------------------
lbl = bwlabel(filled);
stats = regionprops(lbl, 'BoundingBox', 'Area', 'Orientation');
figure, imshow(label2rgb(lbl), []), title('labeled')
% here I just take the largest contour for convenience. should consider aspect ratio and any
% other features that can be used to uniquely identify the shape
[mx, id] = max([stats.Area]);
mxbb = stats(id).BoundingBox;
% resize and rotate the template
tmplre = imresize(tmpl, [mxbb(4) mxbb(3)]);
tmplrerot = imrotate(tmplre, stats(id).Orientation-tmplAngle);
xcr = xcorr2(double(filled), double(tmplrerot));
figure, imshow(xcr, []), title('template matching')
Resized image:
Segmented:
Template matching: