# Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation
## 5 Conclusions  
Analysis of fundus images is vital in diagnosing various diseases whose symptoms appear in the retina of patients. One of the symptoms is the thickening of retinal blood vessels which can be studied through medical image analysis techniques like one proposed in this paper. In this work, we have suggested an unsupervised blood vessel segmentation method using color fundus images. Color fundus images normally suffer from varying and low contrast. To overcome this problem, we pre-processed the color fundus images using CLAHE and FBHE. Furthermore, we enhanced elongated objects using the tophat transform and extract the candidate blood vessel pixels using matched filtering and Gabor wavelet and finally obtained the blood vessel using the human visual system. The proposed system is assessed using two public benchmark datasets named DRIVE and STARE. The obtained mean values Sen, Spe and Acc are 0.7271, 0.9798, 0.9573 and 0.7164, 0.9760, 0.9560, respectively, for the DRIVE and STARE datasets. These measures are similar to current state-of-the-art methods albeit at a smaller computational requirement of 3.17 s.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the simulations; [PERSON] and [PERSON] analyzed the results; [PERSON] and [PERSON] wrote the paper, [PERSON], [PERSON] and [PERSON] technically reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
[PERSON] Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Institutional Review Board Statement: Not applicable.  
Informed Consent Statement: Not applicable.  
Data Availability Statement: The simulations to gauge the performance of suggested technique is done using benchmark public datasets called DRIVE and STARE.  
The authors sincerely appreciate the support from Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **DRIV** & & & **STARE** & \\ \hline
**Method** & **System Specs** & **Acc** & **T in sec** & **Acc** & **T in sec** \\ \hline [PERSON] [22] & 360 GHz Intel Core i7 & 0.9606 & 156 & 0.9435 & 203 \\  & 20 GB RAM & & & & \\ \hline [PERSON] [25] & 2 * Intel Xeon E2620 v4, 64 GB RAM, Nvidia & 0.9450 & 9 & 0.9486 & 9 \\  & Tesla K40 GPU & & & & \\ \hline [PERSON] [30] & GPU machine besides 56 G of RAM and an & 0.9556 & 2.84 & 0.9712 & 6.42 \\  & NIVIDIA GEFORCE GTX-980 Ti. & 0.9577 & 10 & 0.9445 & 10 \\ \hline [PERSON] [33] & Intel Core i5, 32 GB RAM & 0.9507 & 3.3 & 0.9500 & 3.3 \\ \hline [PERSON] [34] & Intel core i3, 1.7 GHZ, 4 GBRAM & 0.9500 & 3.3 & 0.9500 & 3.3 \\ \hline [PERSON] [37] & Core i7, 2.21 GHz, 16 GB RAM & 0.9553 & 5 & 0.9545 & 5 \\ \hline [PERSON] [39] & 2.9 GHz, 64 GB RAM & 0.9480 & 60 & 0.9542 & 60 \\ \hline [PERSON] [40] & Intel Xenon, 2.00 GHz,16 GB RAM & 0.9552 & 3.47 & 0.9543 & 6.10 \\ \hline
**Proposed Method** & **Intel(R) Xeon(R) 3.50 GHz, 32 GB RAM.** & **0.9573** & **3.17** & **0.9560** & **3.17** \\ \hline \end{tabular}
\end{table}
Table 5: Execution time comparison.