# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 5 Experiments
### Evaluating Image Quality
#### 5.2.3 Fsim
Sci._**2024**, _14_, 9785  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**SFO**} & \multicolumn{4}{c}{**SCA**} & \multicolumn{4}{c}{**PSO**} & \multicolumn{4}{c}{**EO**} & \multicolumn{4}{c}{**DE**} & \multicolumn{4}{c}{**GA**} & \multicolumn{4}{c}{**HS**} \\ \cline{2-13} \cline{13-13} \multicolumn{1}{c}{**Cameraman**} & **nt** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \cline{2-13}  & 2 & 1.4117 & 0.01096 & 1.4018 & 0.00015 & 1.6651 & 0.1743 & **1.4017** & 0.00000 & 1.8029 & 0.0612 & 1.4022 & 0.00114 & 1.4477 & 0.08109 \\  & 3 & 0.8469 & 0.0698 & 0.7657 & 0.00118 & 0.9549 & 0.07221 & 0.7683 & 0.00000 & 1.0170 & 0.08735 & 0.7649 & 0.00123 & 0.8745 & 0.14120 \\  & 4 & 0.0654 & 0.0461 & 0.5478 & 0.00534 & 0.8119 & 0.14055 & 0.5385 & 0.00000 & 0.7439 & 0.04196 & 0.5411 & 0.00247 & 0.5816 & 0.04190 \\  & 5 & 0.4578 & 0.03770 & 0.4274 & 0.02373 & 0.6534 & 0.09115 & **0.4040** & 0.00248 & 0.6161 & 0.07341 & 0.4102 & 0.00488 & 0.4449 & 0.0243 \\  & 8 & 0.2606 & 0.0320 & 0.2767 & 0.0326 & 0.3809 & 0.06011 & **0.2064** & 0.00179 & 0.3664 & 0.02945 & 0.2101 & 0.00814 & 0.2547 & 0.00730 \\  & 16 & 0.1025 & 0.01772 & 0.1278 & 0.01575 & 0.15242 & 0.02066 & 0.0669 & 0.00428 & 0.35171 & 0.02406 & 0.0710 & 0.00366 & 0.01133 & 0.0148 \\  & 32 & 0.0404 & 0.0061 & 0.0507 & 0.00650 & 0.0567 & 0.00783 & **0.0186** & 0.00170 & 0.05899 & 0.00774 & 0.0235 & 0.00151 & 0.0398 & 0.00498 \\ \hline  & \multicolumn{4}{c}{**SFO**} & \multicolumn{4}{c}{**SCA**} & \multicolumn{4}{c}{**PSO**} & \multicolumn{4}{c}{**EO**} & \multicolumn{4}{c}{**DE**} & \multicolumn{4}{c}{**GA**} & \multicolumn{4}{c}{**HS**} \\ \cline{2-13} \cline{13-13} \multicolumn{1}{c}{**Lenna**} & **nt** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \cline{2-13}  & 2 & 1.3773 & 0.01259 & 1.3664 & 0.0019 & 1.5693 & 0.14865 & **1.3663** & 0.00000 & 2.9904 & 0.01884 & 1.3666 & 0.00074 & 1.4117 & 0.05143 \\  & 3 & 0.8733 & 0.1432 & 0.7201 & 0.00227 & 0.9636 & 0.05959 & 0.7174 & 0.00000 & 1.1725 & 0.04331 & 0.7190 & 0.00160 & 0.7610 & 0.05498 \\  & 4 & 0.5989 & 0.08566 & 0.5133 & 0.08311 & 0.07460 & 0.19945 & 0.4687 & 0.00000 & 0.8588 & 0.40771 & 0.4732 & 0.00431 & 0.5256 & 0.06262 \\  & 5 & 0.4730 & 0.09972 & 0.37571 & 0.04824 & 0.6213 & 0.11400 & 0.3272 & 0.00012 & 0.6493 & 0.07692 & 0.3377 & 0.00456 & 0.3901 & 0.05983 \\  & 8 & 0.2775 & 0.0431 & 0.2478 & 0.03314 & 0.3437 & 0.05068 & 0.16690 & 0.00948 & 0.3879 & 0.4012 & 0.1771 & 0.00970 & 0.2333 & 0.04870 \\  & 16 & 0.1004 & 0.01243 & 0.1319 & 0.01462 & 0.1425 & 0.02351 & 0.05898 & 0.00429 & 0.1425 & 0.01876 & 0.06491 & 0.00413 & 0.01373 & 0.01466 \\  & 32 & 0.0084 & 0.00503 & 0.0444 & 0.00625 & 0.0266 & 0.0060 & 0.01715 & 0.00160 & 0.0576 & 0.0691 & 0.0208 & 0.00097 & 0.0404 & 0.00462 \\ \hline  & \multicolumn{4}{c}{**SFO**} & \multicolumn{4}{c}{**SCA**} & \multicolumn{4}{c}{**PSO**} & \multicolumn{4}{c}{**EO**} & \multicolumn{4}{c}{**DE**} & \multicolumn{4}{c}{**GA**} & \multicolumn{4}{c}{**HS**} \\ \cline{2-13} \cline{13-13} \multicolumn{1}{c}{**Baboon**} & **nt** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \cline{2-13}  & 2 & 1.2168 & 0.01205 & 1.2050 & 0.00018 & 1.3544 & 0.16955 & **1.2049** & 0.00000 & 1.3460 & 0.1567 & 1.2055 & 0.00084 & 1.2335 & 0.06351 \\  & 3 & 0.8669 & 0.11554 & 0.7433 & 0.00184 & 0.9427 & 0.07690 & **0.7407** & 0.00000 & 0.8646 & 0.05344 & 0.7426 & 0.00293 & 0.7963 & 0.07087 \\  & 4 & 0.6093 & 0.1067 & 0.5193 & 0.00557 & 0.26136 & 0.12657 & **0.5073** & 0.00001 & 0.6382 & 0.07473 & 0.51413 & 0.00435 & 0.5565 & 0.05481 \\  & 5 & 0.4853 & 0.07322 & 0.4100 & 0.04572 & 0.6102 & 0.08255 & 0.3651 & 0.00017 & 0.4888 & 0.4138 & 0.3821 & 0.00829 & 0.0149 & 0.0386 \\  & 8 & 0.2767 & 0.04355 & 0.2663 & 0.02277 & 0.3175 & 0.0 The numerical results demonstrate that EO significantly outperformed other stochastic optimization algorithms when applied to the image thresholding problem using cross-entropy.