# An Image Segmentation Method Based on Improved Regularized Level Set Model
## 4 Experimental Results
### Discussion  
According to the abovementioned experimental results, our contributions to the IRLS-IS method can be summarized as follows. (1) Compared with [PERSON]'s DRLSE model in [18], our proposed IRLS-IS model initializes the level set function to a constant, which solves the problem of selecting the initial size of the level set and is insensitive to the initialization. The IRLS-IS model can eliminate the dependence on this initial contour position so that it can segment images effectively. However, the DRLSE model is overly reliant on the initialization location. Therefore, some numerical errors may occur during the evolution, and occasionally the desired segmentation results may not be derived [84; 85; 86]. Because only the local region information is used, the LBF method efficiently segments the inhomogeneous images. However, it is very easy to get caught up in a local minimum when this initial contour is inappropriate. Thus, the LBF method is sensitive to the initial contour of the images. In contrast to LBF, LIF conducts the convolution before the iteration, and thus, the running time of the computing can be reduced dramatically [87; 88], but the initial contour of LBF and LIF is weak in robustness. The global information-based CEM model is the same as the CV model and cannot achieve the satisfied segmentation results on some inhomogeneous images. (2) The LoG filter-based external energy term is presented. The external energy term not only reduces the influence of noise on image segmentation but also overcomes the interference caused by intensity inhomogeneity. Moreover, the internal energy term is introduced to regulate the smoothness of the zero level curves and to eliminate the low occurrence and isolated regions in the final segmentation results [23]. (3) The new distance regularization term corrects this deviation of the traditional level set function and the SDF to avoid the expensive computation of reinitialization. In addition, the new distance regularization term makes the IRLS-IS method evolve in a large step, and thus, the IRLS-IS method can \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Image ID**} & \multicolumn{2}{c}{**CV**} & \multicolumn{2}{c}{**LBF**} & \multicolumn{2}{c}{**LIF**} & \multicolumn{2}{c}{**IRLS-IS**} \\ \cline{2-9}  & _Dice_ & _JSI_ & _Dice_ & _JSI_ & _Dice_ & _JSI_ & _Dice_ & _JSI_ \\ \hline
8068 & 0.9780 & 0.9570 & 0.9555 & 0.9149 & 0.8673 & 0.7657 & **0.9792** & **0.9592** \\
17067 & 0.9093 & 0.8336 & 0.8783 & 0.7830 & 0.8303 & 0.7099 & **0.9425** & **0.8912** \\
28083 & 0.9469 & 0.8991 & 0.9236 & 0.8580 & 0.8062 & 0.6753 & **0.9556** & **0.9151** \\
29030 & 0.9525 & 0.9093 & 0.9432 & 0.8925 & 0.8106 & 0.6815 & 0.9634 & 0.9293 \\
33044 & 0.9108 & 0.8361 & 0.8700 & 0.7699 & 0.8253 & 0.7026 & **0.9434** & **0.8929** \\
41004 & 0.9763 & 0.9537 & 0.9565 & 0.9166 & 0.8769 & 0.7808 & **0.9769** & **0.9548** \\
41085 & 0.9209 & 0.8534 & 0.8818 & 0.7886 & 0.8446 & 0.7310 & **0.9229** & **0.8568** \\
86016 & 0.9345 & 0.8770 & 0.7868 & 0.6485 & 0.7481 & 0.5976 & **0.9536** & **0.9113** \\
102061 & **0.9634** & **0.9293** & 0.9442 & 0.8944 & 0.8516 & 0.7416 & 0.9633 & 0.9291 \\
135069 & 0.9914 & 0.9829 & 0.9909 & 0.9819 & 0.8202 & 0.6951 & **0.9950** & **0.9900** \\
143090 & 0.9575 & 0.9185 & 0.9517 & 0.9083 & 0.8633 & 0.7595 & **0.9715** & **0.9446** \\
147091 & **0.9693** & **0.9404** & 0.9387 & 0.8844 & 0.8254 & 0.7027 & 0.9673 & 0.9367 \\
207056 & 0.9677 & 0.9375 & 0.9305 & 0.8700 & 0.8183 & 0.6924 & **0.9874** & **0.9482** \\
296059 & 0.9470 & 0.8994 & 0.9276 & 0.8650 & 0.8283 & 0.7069 & **0.9658** & **0.9339** \\
317080 & 0.9591 & 0.9214 & 0.9288 & 0.8671 & 0.8665 & 0.7645 & **0.9596** & **0.9223** \\ \hline \end{tabular}
\end{table} 
Table 1: The _Dice_ and _JSI_ values of the results of image segmentation on the fifteen Berkeley color images. _JSI_: Jaccard similarity index; CV: [PERSON]. improve the efficiency and generate the results of image segmentation.