Hi, i'm new in this forum and i'd ask you some help; i'm trying to solve a problem in my university thesis but i need some ideas; i'm working with retinal images and i'm trying to detect the vessels in a automatic way with Matlab software; i tried to improve the image working in the contrast and brightness but now i have some problems about the accurate recognition of the single vessels. I tried to use K-means and fuzzy c-means algorithm but the results aren't so good.
Someone can give me help please??
Dear guest, you must be logged-in to participate on CrazyEngineers. We would love to have you as a
member of our community. Consider creating an
account or login.
I used this code: imm_temp=double(f_analyzed); m=min(imm_temp😀));
M=max(imm_temp😀));
betcontr_imm=255*(imm_temp-m)/(M-m);
betcontr_imm=uint8(betcontr_imm);
betcontr_immlog=mat2gray(log(1 + double(f_analyzed)));
I used two type of way to improve the contrast but the better solution is the logarithmic one.
the situation is this; i have a retinal image of the blood vessels and the seg structure with all the crossover and the bifurcations. I have to create an automatic algorithm that analyzes all the cross.. and bifurc... that improves the result of the tracking algorithm that gave me the seg structure with all the coordinates of the bifurc and crossover. I read a lot of papers but i don't understand how to proceed and what type of algorithm is best for my work.
To solve my problem i take all the single big and cross and analyze them alone, so i improve the contrast and use the Laplacian and logarithmic filters with this windows
w4=fspecial('laplacian',0);
w8=[1 1 1; 1 -8 1; 1 1 1];
wlog=fspecial('log');
The best solution i can see is the w8 but it depends in particular by the image that i'm analyzing.