# Gaslike Social Motility: Optimization Algorithm with Application in Image Thresholding Segmentation
## 4 GSM Algorithm Application to Threshold Segmentation
### Results and Discussion Related to the Segmentation Application
\begin{table}
\begin{tabular}{l l l} \hline
**Image** & **Th** & **Results** \\ \hline Boat & 2 & 69 132 \\  & 4 & 46 87 131 167 \\  & 8 & 24 41 61 86 113 136 157 186 \\  & 16 & 16 25 35 47 63 79 95 112 127 139 150 162 176 195 207 214 \\ \hline House & 2 & 81 151 \\  & 4 & 54 87 129 180 \\  & 8 & 41 59 84 106 127 154 189 222 \\  & 16 & 24 61 58 72 86 99 106 112 120 133 150 167 182 196 212 225 \\ \hline Airplane & 2 & 94 160 \\  & 4 & 62 106 149 192 \\  & 8 & 40 62 84 103 122 150 180 204 \\  & 16 & 17 22 31 43 58 74 87 100 112 124 138 156 175 191 203 213 \\ \hline Lake & 2 & 73 141 \\  & 4 & 59 01 142 \\  & 8 & 40 54 70 93 122 154 177 202 \\  & 16 & 7 28 38 46 53 62 74 86 98 112 129 147 165 180 194 213 \\ \hline Tank & 2 & 61 102 \\  & 4 & 43 76 101 123 \\  & 8 & 32 50 64 77 90 105 120 133 \\  & 16 & 30 46 56 61 69 78 85 93 102 109 118 123 130 136 142 153 \\ \hline Couple & 2 & 73 134 \\  & 4 & 37 82 123 160 \\  & 8 & 19 41 65 89 112 132 154 183 \\  & 16 & 7 16 29 42 54 64 76 89 100 110 121 132 145 159 177 203 \\ \hline Peppers & 2 & 51 123 \\  & 4 & 35 74 115 161 \\  & 8 & 14 30 56 80 102 125 150 177 \\  & 16 & 14 24 30 42 53 63 71 80 90 104 118 131 147 162 174 192 \\ \hline Truck & 2 & 92 134 \\  & 4 & 68 94 121 150 \\  & 8 & 49 67 82 98 115 134 150 163 \\  & 16 & 36 41 49 57 65 74 81 88 95 102 109 119 128 139 149 163 \\ \hline Hunter & 2 & 73 128 \\  & 4 & 62 98 130 162 \\  & 8 & 53 73 95 118 141 165 181 202 \\  & 16 & 41 47 57 68 74 81 89 100 111 122 133 145 158 170 191 223 \\ \hline \end{tabular}
\end{table} 
Table 11: Comparative study of the fitness values, considering MCET as objective function. \begin{table}
\begin{tabular}{l l l l l} \hline
**Image** & **Th** & **Best Fitness** & **Mean Fitness** & **Std** \\ \hline Boat & 2 & \(-645.5921485\) & \(-645.5920\) & 0.0004 \\  & 4 & \(-646.4887625\) & \(-646.4847\) & 0.0137 \\  & 8 & \(-646.9336700\) & \(-646.9192\) & 0.0149 \\  & 16 & \(-647.0805553\) & \(-647.0692\) & 0.0087 \\ \hline House & 2 & \(-689.070058\) & \(-689.09707\) & 0.0000 \\  & 4 & \(-689.5663172\) & \(-689.5309\) & 0.0792 \\  & 8 & \(-689.7855957\) & \(-689.7774\) & 0.0145 \\  & 16 & \(-689.8643191\) & \(-689.8633\) & 0.0008 \\ \hline Airplane & 2 & \(-934.9338252\) & \(-934.9338\) & 0.0000 \\  & 4 & \(-935.4267241\) & \(-935.4259\) & 0.0019 \\  & 8 & \(-935.6618385\) & \(-935.6541\) & 0.0061 \\  & 16 & \(-935.7293579\) & \(-935.7130\) & 0.0211 \\ \hline Lake & 2 & \(-622.7141857\) & \(-622.7141\) & 0.0001 \\  & 4 & \(-623.5186087\) & \(-623.5182\) & 0.0006 \\  & 8 & \(-623.946695\) & \(-623.9303\) & 0.0192 \\  & 16 & \(-624.0848274\) & \(-624.0810\) & 0.0035 \\ \hline Tank & 2 & \(-509.474399\) & \(-509.4741\) & 0.0008 \\  & 4 & \(-509.929904\) & \(-509.9279\) & 0.0038 \\  & 8 & \(-510.113068\) & \(-510.1014\) & 0.0098 \\  & 16 & \(-510.174674\) & \(-510.8449\) & 0.0091 \\ \hline \end{tabular}
\end{table} 
Table 10: Best threshold values. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(Th=2\) & \(Th=4\) & \(Th=8\) & \(Th=16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Segmented images (Boat, Tank, and House) with proposed GSM algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(Th=2\) & \(Th=4\) & \(Th=8\) & \(Th=16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Segmented images (Hunter, Jetplane, and Peppers) with the proposed GSM algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(Th=2\) & \(Th=4\) & \(Th=8\) & \(Th=16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Segmented images (Couple, Truck, and Lake) with proposed GSM algorithm.