Abstract
Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF- and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.
Highlights
Image segmentation is an important task in the field of image analysis and object detection and aims to segment an image into distinctive subregions that are meaningful to analyze [1]
The SPFLIF-IS method is consistently compared with the five abovementioned methods (C–V, local binary fitting (LBF), SBGFRLS, local image fitting (LIF), and LSACM)
The true boundaries of the third image cannot be extracted by the Chan and Vese (C–V) model, the LBF model, the SBGFRLS model, or the LIF model; the results are shown in Row 3 of Figure 3
Summary
Image segmentation is an important task in the field of image analysis and object detection and aims to segment an image into distinctive subregions that are meaningful to analyze [1]. Segmentation is the intermediate step between image processing and image analysis as well as the bridge from lowto high-level research in computer vision. Inhomogeneity, noise, and low contrast in real images have increased the difficulty of image segmentation [2]. Over the past few decades, many segmentation methods have been proposed. The active contour model (ACM), which was proposed by Kass et al [3], has been proven to be an efficient framework for image segmentation. The fundamental idea of the ACM framework is to control a curve to move toward its interior normal and stop on the true boundary of an object based on an energy minimization model [4]. Existing ACM methods can be roughly divided into the following types, edge-based models [6,7,8,9] and region-based models [10,11,12,13,14]
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