This is a very challenging work due to the ambiguous boundaries and tiny difference between red and green intensities. If you want to implement the segmentation very precisely and meet some medical requirements, Shai's k-means plus graph cuts may be one of the very few options (EM algorithm may be an alternative). If you have a large database that has many similar images, some machine learning methods might help. Otherwise, I just wrote a very simple code to roughly extract the internal red region for you. The boundary is not that accurate since some of the green regions are also included.
I1=I;
I=rgb2hsv(I);
I=I(:,:,1); % the channel with relatively large margin between green and red
I=I.*(I<0.25);
I=imdilate(I, true(5));
% I=imfill(I,'holes'); depends on what is your definition of the inner boundary
bw=bwconncomp(I);
ar=bw.PixelIdxList;
% find the largest labeled area,
n=0;
for i=1:length(ar)
if length(ar{i})>n
n=length(ar{i});
num=i;
end
end
bw1=bwlabel(I);
bwfinal(:,:,1)=(bw1==num).*double(I1(:,:,1));
bwfinal(:,:,2)=(bw1==num).*double(I1(:,:,2));
bwfinal(:,:,3)=(bw1==num).*double(I1(:,:,3));
bwfinal=uint8(bwfinal);
imshow(bwfinal)