# A Novel Evolution Strategy of Level Set Method for the Segmentation of Overlapping Cervical Cells
## 4 Results and Discussion
### Quantitative Evaluation of Our Segmentation Results
#### 4.2.3 Computational Complexity  
We made an average running time comparison between our proposed algorithm and [33] on the test-90 dataset based on the hardware platform with a 2.30 GHz Intel Core i7-4712 MQ processor and 12 GB DDR3 RAM in the form of an unoptimized MATLAB (The MathWorks, Inc., Natick, MA, USA) code. Figure 10a shows the running time of the initialization described in Section 2.1, which takes 0.14-1.5 min, with an average of 0.79 (std: 0.37) minutes. Figure 10b,c show the running time of [33] and us, respectively, in segmenting cytoplasm contours from the background based on the initialization work. Our proposed algorithm is 2-26 times faster than [33], with an average of 5.4 (std: 4.0) times acceleration for the images on the test-90 dataset. Moreover, when the overlap ratio ranges [0.01-[0.4.0.5], the running time is reduced by 10.4 (std: 6.4), 5.1 (std: 2.7), 4.5 (std: 1.5), 4.1 (std: 1.0) and 3.2 (std: 0.6) times, respectively. When the number of cells ranges 2-10, the running time is reduced by 7.3 (std: 4.2), 3.7 (std: 0.5), 5.3 (std: 3.4), 8.6 (std: 9.6), 5.3 (std: 3.4), 4.2 (std: 2.0), 4.3 (std: 1.6), 5.6 (std: 2.3) and 4.8 (std: 2.6) times.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **FN\({}_{\text{O}}\)** & **TP\({}_{\text{P}}\)** & **FP\({}_{\text{P}}\)** & **DC** \\ \hline \multicolumn{5}{c}{ISBI-14 Train-45 Dataset} \\ [PERSON] [23] & 0.267(0.278) & 0.841(0.130) & **0.002(0.003)** & 0.872(0.082) \\ [PERSON] [30] & 0.111(0.166) & 0.875(0.086) & 0.004(0.004) & 0.871(0.075) \\ [PERSON] [28] & 0.296(0.277) & **0.948(0.059)** & 0.005(0.007) & 0.914(0.075) \\ [PERSON] [33] & 0.148(0.206) & 0.920(0.080) & 0.005(0.007) & 0.912(0.066) \\ [PERSON] [26] & 0.137(0.194) & 0.882(0.097) & **0.002(0.003)** & 0.897(0.075) \\ Ours & **0.104(0.126)** & 0.931(0.065) & 0.004(0.006) & **0.923(0.059)** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results (standard deviation) of our cytoplasm segmentation and the state-of-the-art methods on the train-45 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **FN\({}_{\text{O}}\)** & **TP\({}_{\text{P}}\)** & **FP\({}_{\text{P}}\)** & **DC** \\ \hline \multicolumn{5}{c}{ISBI-14 Test-90 Dataset} \\ [PERSON] [23] & 0.174(0.210) & 0.826(0.130) & 0.001(0.002) & 0.867(0.083) \\ [PERSON] [30] & 0.140(0.170) & 0.900(0.090) & 0.005(0.004) & 0.870(0.080) \\ [PERSON] [31] & 0.110(0.170) & 0.930(0.090) & 0.005(0.004) & 0.880(0.080) \\ [PERSON] [33] & 0.317(0.284) & 0.895(0.102) & 0.003(0.006) & 0.891(0.082) \\ [PERSON] [52] & 0.163(0.223) & 0.939(0.064) & 0.005(0.005) & 0.888(0.076) \\ [PERSON] [53] & 0.274(0.277) & 0.907(0.088) & 0.004(0.005) & 0.889(0.073) \\ [PERSON] [50] & 0.222(0.240) & **0.945(0.071)** & 0.005(0.005) & 0.897(0.077) \\ [PERSON] [51] & **0.100(0.130)** & 0.940(0.090) & 0.004(0.003) & 0.890(0.070) \\ [PERSON] & 0.281(0.226) & 0.911(0.085) & **0.003(0.005)** & **0.904(0.073)** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results (standard deviation) of our cytoplasm segmentation and the state-of-the-art methods on the test-90 dataset.