# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 1 Introduction
In addition, the model is sensitive to the initialization location and parameters [5; 26], [PERSON] et al. [27] defined an energy functional that combines the merits of the C-V model and the LBF model [21]. Because the new model employs local and global intensity information, it can avoid becoming trapped in a local minimum; however, the result remains partially dependent on the initialization location [21]. [PERSON] et al. [28] exploited a local image region statistics-based improved ACM method (LSACM) in the presence of intensity inhomogeneity. The LSACM is robust to noise while suppressing intensity overlap to some extent. [PERSON] et al. [25] offered a model based on global and local regions. The global term takes gradient amplitude into consideration, and the local term adopts local image information by convolving the Gaussian kernel function [29]. This algorithm is sensitive to the initialization location because of the use of gradient information. Similarly, [PERSON] et al. [30] adopted local region statistical information and gradient information to construct an energy functional and faced the same problem. [PERSON] et al. [31] introduced a local image fitting (LIF) energy functional to extract local image information and proposed a Gaussian filtering method for a variational level set to regularize the level set function, which can be interpreted as a constraint on the differences between the original image and the fitting image [12; 24]. Furthermore, the method used Gaussian kernel filtering to regularize the level set function, and a reinitialization operation was avoided [32]. Unfortunately, the abovementioned methods are sensitive to initialization, and they are also unable to analyze images with intensity inhomogeneity. Hence, these limitations obviously limit their practical applications. Here, we focus on overcoming these drawbacks in this paper. In this study, to segment the images quickly and accurately, a new image segmentation model is proposed based on an improved SPF and LIF. This method defines a new SPF function, which uses global image information, and the SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced, which is based on local image information, and this model is used to segment intensity-inhomogeneous images. Moreover, a weight function is established to adjust the weights between the SPF model and the LIF model. Thus, a novel ACM model is presented, and an image segmentation algorithm is investigated. Experimental results demonstrate that our model involves simpler computation, exhibits faster convergence, and can effectively segment multiobjective images and intensity-inhomogeneous images. Furthermore, the proposed method is highly robust to the initial contour and noise. The remainder of this paper is structured as follows. Section 2 briefly reviews the GAC, C-V, SBGFRLS, and LIF models. In Section 3, by combining the improved SPF function with the LIF model, a novel ACM is presented, and using this model, an image segmentation algorithm is designed. Then, the experimental results and analysis are discussed in Section 4. Section 5 presents the conclusions.