# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 4 Experimental Results
### Comparative Evaluation Results  
In addition to using visual evaluation, the accuracy of the target region segmentation can be assessed quantitatively and objectively using the _DICE_ coefficient (_DICE_) [51; 52] and the Jaccard similarity index (_JSI_) [53]. Following the experimental techniques designed in [42; 54], test images are selected randomly from the BSDS500 database. Note that BSDS500 contains hundreds of natural images whose ground-truth segmentation maps have been generated by multiple individuals [40; 55]. To enhance the coherency of our work with the abovementioned algorithms, three comparative experiments are performed on many real-world color images, which are selected from the Berkeley segmentation data set 500 (BSDS500) and consist of a set of natural images. The first part of this experiment involves evaluating the value of the _DICE_ for twenty representative real-world color images, which are chosen from the Berkeley segmentation data set 500 (BSDS500). The algorithms compared are the C-V model [34], the LBF model [24], and the LIF model [31]. The _DICE_, also called the overlap index, is the most frequently used metric for validating image segmentations. The _DICE_ measures how well the segmentation results \(S\) match the ground truth \(G\). When the value of the _DICE_ is close to 1, the segmentation results have high accuracy. The formula for the _DICE_ is given as  
\[DICE(G,S)=\frac{2|\Omega_{G}\cap\Omega_{S}|}{|\Omega_{G}|+|\Omega_{S}|}, \tag{25}\]  
where \(\Omega_{S}\) describes the segmented volume and \(\Omega_{G}\) denotes the ground truth [56; 57]. The _DICE_ values of the segmentation results obtained by applying the four models to segment Berkeley color images are listed in Table 6, where the Mean describes the average values of the _DICE_ for all test image data. Table 6 shows that the SPFLIF-IS method yields the best values for the _DICE_ on the twenty image data, and the corresponding Mean is also the largest. The results indicate that our SPFLIF-IS model outperforms the C-V, LBF, and LIF models. In summary, these results demonstrate that our SPFLIF-IS method is indeed efficient and outperforms these currently available approaches. Figure 6: The segmentation results obtained for real images using the six models. (**a**) Original image, (**b**) C–V model, (**c**) LBF model, (**d**) SBGFRLS model, (**e**) LIF model, (**f**) LASACM model, and (**g**) SPFLIF-IS model. The next section of the experiment involves testing the value of the _JSI_ coefficient for the twenty representative real-world color images in Table 6. The algorithms compared are still the C-V model [34], the LBF model [24], and the LIF model [31]. The _JSI_ is the second statistical measure used for quantitative evaluation in this paper. The _JSI_ is calculated by  
\[JSI(G,S)=\frac{|\Omega_{G}\cap\Omega_{S}|}{|\Omega_{G}\cup\Omega_{S}|}. \tag{26}\]  
The accuracy of the segmentation results for the Berkeley color images is measured by the _JSI_ value, as shown in Figure 7. A _JSI_ value close to 1 indicates favorable segmentation results. Figure 7 shows that the SPFLIF-IS method exhibits the best _JSI_ values for the twenty image data. For image IDs 3063, 14092, 41006, and 147091, the _JSI_ values of the C-V model are very close to those of the SPFLIF-IS model. For image ID 227092, the _JSI_ values yielded by the C-V and LBF models are close to those of the SPFLIF-IS model. However, Figure 7 clearly illustrates that the SPFLIF-IS method yields greater _JSI_ values than those generated by the C-V, LBF, and LIF models. In the final part of this experiment, to fully validate the advantages of our SPFLIF-IS method in terms of the _DICE_ and _JSI_, the five state-of-the-art methods ((1)) the C-V model [34], (2) the LBF model [24], (3) the LIF model [31], (4) the SBGFRLS model [23], and (5) the LSACM model [28]) are applied to eight real color image data selected from the Berkeley segmentation data set 500 (BSDS500).