# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
## 5 Experimental Analysis
### Segmentation Experiment and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric Name** & **Image** & **k** & **WOA** & **BOA** & **PSO** & **OURS** \\ \hline \multirow{8}{*}{Image97(001236)} & & 2 & **2750.8992** & 2750.7616 & 2750.1240 & 2750.7641 \\ \cline{2-6}  & & 4 & **3517.7437** & 3517.7256 & 3517.3212 & 3517.7290 \\ \cline{2-6}  & & 6 & **4509.9853** & 4509.7814 & 4509.5764 & 4509.7829 \\ \cline{2-6}  & & 8 & **6495.5661** & 6495.5018 & 6493.2953 & 6495.5538 \\ \cline{2-6}  & & 2 & **2338.4961** & 2338.3209 & 2338.2888 & 2338.4402 \\ \cline{2-6}  & & 4 & **321.9846** & 3251.4460 & 3251.2548 & 3251.7075 \\ \cline{2-6}  & & 6 & **3637.9811** & 3637.4199 & 3637.1219 & 3637.6122 \\ \cline{2-6}  & & 8 & **4735.8050** & 4735.3475 & 4734.1491 & 4735.5701 \\ \hline \multirow{8}{*}{Fitness} & & 2 & **2779.9633** & 2777.8345 & 2777.1929 & 2777.9091 \\ \cline{2-6}  & & 4 & **3709.8962** & 3709.2028 & 3709.1977 & 3709.6104 \\ \cline{2-6}  & & 6 & **4837.6270** & 4837.2012 & 4837.1285 & 4837.5268 \\ \cline{2-6}  & & 8 & **5453.6888** & 5453.5749 & 5453.3442 & 5453.6073 \\ \cline{2-6}  & & 2 & **1699.8490** & 1699.3038 & 1699.1601 & 1699.7288 \\ \cline{2-6}  & & 4 & **2878.5953** & 2878.2350 & 2878.1299 & 2878.2976 \\ \cline{2-6}  & & 6 & **3708.6870** & 3708.4114 & 3708.4016 & 3708.4181 \\ \cline{2-6}  & & 8 & **4828.7905** & 4828.5773 & 4825.4886 & **4828.9096** \\ \hline \multirow{8}{*}{Image11(004610)} & & 2 & **1343.9485** & 1343.6048 & 1343.4410 & 1343.7570 \\ \cline{2-6}  & & 4 & **2561.8768** & 2561.4219 & 2561.3565 & 2561.2494 \\ \cline{2-6}  & & 6 & **2905.9755** & 2905.5645 & 2905.5418 & 2905.7108 \\ \cline{2-6}  & & 8 & **4101.8497** & 4101.5807 & 4100.4235 & 4101.6487 \\ \cline{2-6}  & & 2 & **2200.6262** & 2200.1469 & 2200.1288 & 2200.5102 \\ \cline{2-6}  & & 4 & **3164.7962** & 3164.2352 & 3164.2936 & 3164.7918 \\ \cline{2-6}  & & 6 & **3932.8209** & 3932.2591 & 3932.2245 & 3932.3805 \\ \cline{2-6}  & & 8 & **4459.9945** & 4459.8215 & 4458.1837 & 4459.9166 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of Fitness in Images g–l. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric Name** & **Image** & **k** & **WOA** & **BOA** & **PSO** & **OURS** \\ \hline \multirow{8}{*}{Image97(001236)} & & 2 & 0.7066 & 0.7277 & 0.7510 & **0.7889** \\ \cline{2-6}  & & 4 & 0.7397 & 0.7645 & 0.7916 & **0.8246** \\ \cline{2-6}  & & 6 & 0.7560 & 0.8089 & 0.8433 & **0.8760** \\ \cline{2-6}  & & 8 & 0.8030 & 0.8279 & 0.8660 & **0.8982** \\ \cline{2-6}  & & 2 & 0.6461 & 0.6516 & 0.6747 & **0.6580** \\ \cline{2-6}  & & 4 & 0.6642 & 0.6737 & 0.6927 & **0.7071** \\ \cline{2-6}  & & 6 & 0.6823 & 0.6957 & 0.7182 & **0.7419** \\ \cline{2-6}  & & 8 & 0.7128 & 0.7246 & 0.7584 & **0.8142** \\ \hline \multirow{8}{*}{FSIM} & & 2 & 0.6726 & 0.6949 & 0.7191 & **0.7347** \\ \cline{2-6}  & & 4 & 0.6938 & 0.7144 & 0.7341 & **0.7694** \\ \cline{2-6}  & & 6 & 0.7220 & 0.7379 & 0.7550 & **0.7977** \\ \cline{2-6}  & & 8 & 0.7421 & 0.7518 & 0.7796 & **0.8084** \\ \cline{2-6}  & & 2 & 0.6381 & 0.6679 & 0.6980 & **0.7130** \\ \cline{2-6}  & & 4 & 0.6526 & 0.6729 & 0.7111 & **0.7368** \\ \cline{2-6}  & & 6 & 0.6932 & 0.7274 & 0.7510 & **0.7787** \\ \cline{2-6}  & & 8 & 0.7349 & 0.7564 & 0.7860 & **0.8072** \\ \hline \multirow{8}{*}{Image11(004610)} & & 2 & 0.7283 & 0.7484 & **0.7585** & 0.7467 \\ \cline{2-6}  & & 4 & 0.7379 & 0.7513 & 0.7685 & **0.7760** \\ \cline{2-6}  & & 6 & 0.7451 & 0.7710 & 0.7819 & **0.8029** \\ \cline{2-6}  & & 8 & 0.7670 & 0.7850 & 0.8129 & **0.8558** \\ \cline{2-6}  & & 2 & 0.7375 & 0.7582 & 0.7955 & **0.8250** \\ \cline{2-6}  & & 4 & 0.7541 & 0.7763 & 0.8152 & **0.8430** \\ \cline{2-6}  & & 6 & 0.7796 & 0.8097 & 0.8421 & **0.8654** \\ \cline{2-6}  & & 8 & 0.8016 & 0.8217 & 0.8427 & **0.8693** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of FSIM in Images g–l. From Tables 3-10, it can be seen that the segmentation effect of the proposed algorithm is better.