# An Image Segmentation Method Based on Improved Regularized Level Set Model
## 4 Experimental Results
### Segmentation of Medical Images  
This part of our experiments is to verify the segmentation performance of medical images. First, we chose two simple medical images to demonstrate the segmentation performance of our IRLS-IS method. Figure 6 shows the original images and the segmentation results of the five compared methods, where the original simple medical images can be obtained from [42; 75], while the compared models are still DRLSE, LBF, LIF, and LSACM. The compared four models cannot segment the first image, and the results are shown in Row 1 of Figure 6. The second image in the second column shows that most of the boundaries are obtained by the DRLSE model. However, the internal details are not recognized; the results are illustrated in Figure 6b. The object boundaries are not accurately extracted by the LBF, LIF, and LSACM algorithms, as depicted in Figure 6c,d,e. The IRLS-IS model initializes the level set function as a constant, and this gets rid of the dependency on the initialization position, so the proposed IRLS-IS method can process some simple medical images efficiently. The segmentation results demonstrate that our IRLS-IS model achieves satisfactory results for simple medical images. Next, we continued testing our algorithms with two types of complex medical images. It is still a challenge to segment some complex medical images, because these medical images not only contain noise and intensity inhomogeneity but also have less obvious boundaries. To verify the efficiency  
Figure 5: Comparisons of the five models in segmenting noisy images: (**a**) The original image; (**b**) The segmentation results of the DRLSE model; (**c**) The segmentation results of the LBF model; (**d**) The segmentation results of the LIF model; (**e**) The segmentation results of the LSACM model; (**f**) The segmentation results of the IRLS-IS model. Figure 6: Comparisons of the five models in segmenting simple medical images: (**a**) The original image; (**b**) The segmentation results of the DRLSE model; (**c**) The segmentation results of the LBF model; (**d**) The segmentation results of the LIF model; (**e**) The segmentation results of the LSACM model; (**f**) The segmentation results of the IRLS-IS model. and feasibility of our proposed IRLS-IS method on complex medical images, three existing methods, namely, the LSACM algorithm [51], the cross entropy-based model (CEM) [46] and the local hybrid image fitting model (LHIF) [77], are selected to conduct further comparative experiments, and the results are presented in Figure 7, where the original image Figure 7a is from [34; 77] and Figure 7c,d were previously obtained by [PERSON] et al. [77]. Figure 7 presents experimental results of heart image segmentation with the four compared models. The image size is 152 \(\times\) 128. As shown in Figure 7b, the LSACM cannot extract the true boundary of the heart image. Figure 7c illustrates that the CEM model has very poor performance in segmenting the heart image. The true boundaries are precisely obtained by [PERSON]'s LHIF model and our proposed IRLS-IS model, and the results are described in Figure 7d,e, respectively. There is a slight difference between the segmentation results of the LHIF model and the IRLS-IS model. Local leakage exists in the upper part of Figure 7d, but this is not the case in our IRLS-IS model. However, our model did not recognize the lower part of the right of Figure 7a, primarily because our algorithm has a large time step. Therefore, the experimental studies pertaining to the heart image state that the IRLS-IS algorithm efficiently eliminates the interference of intensity inhomogeneity and can achieve better segmentation performance on this medical image of a heart. When segmenting images with a cerebral infarction, there are two relatively sensitive problems with the traditional level set model [78; 79]. One problem is that the boundaries between brain tissues are often not characterized by class changes; therefore, the boundary is prone to leakage.