Research On RSF Model Image Segmentation Algorithm Based On Fractional Order Differentiation

Image segmentation plays an important role in computer vision research, an ideal segmentation result is helpful for one to analyze and understand image. In the process of actual application, the factors come from imaging equipment or environment may result in image noise, intensity inhomogeneities, fuzzy edge and texture. However, the traditional method is limited to obtain ideal segmentation results in these cases. Recently, the RSF (Region-Scalable Fitting) algorithm based on the active contour model has been widely used in image segmentation. But the RSF model has some limitations in the process of optimization, such as it is sensitive to the initial location of evolution curve, blurred image boundary, and easy to fall into local optimum. Aiming at these problems, this paper tries to deal with the limitations as described above, detailed research contents and achievements are as follows:(1) RSF model is sensitive to the initial location of evolution curve, it tends to fall into local optimal with slow evolution. Aims at this problem, this paper adds the global Grumwald-Letnikov(G-L) fractional gradient into the RSF model, which can strengthen the gradient of the intensity inhomogeneities and weak texture regions. As a result, both the robustness of initial location of evolution curve and efficiency of image segmentation are improved. In addition, the relation curve between order and segmentation accuracy is given, which indicates that the order of getting correct segmentation is between 0-1. Theoretical analysis and experimental results show that the proposed algorithm is capable of segmenting the intensity inhomogeneities and weak texture images. It can deal with the problem that RSF model is sensitive to initial location of evolution curve, and improve the efficiency of segmentation and robustness to image noise.(2) There exit fuzzy boundaries in the RSF model application process, as well, after the applying of G-L fractional order, it is still keep open to get the optimal fractional order. This paper uses bilateral filter to substitute the Gauss kernel function in local fitting items, which can enhance the performance of edge preserving and avoid fuzzy boundaries in the evolution process caused by the Gauss kernel function, thus the boundary localization ability of RSF model is improve. Then an adaptive fractional order mathematical model is constructed based on the gradient modulus value and information entropy of image, therefore the optimal fractional order is calculated adaptively. Theoretical analysis and experimental results show that the proposed algorithm is capable of segmenting the intensity inhomogeneities and weak texture images. It is capable of getting an optimal fractional order adaptively in term of the image's local features, and avoiding falling into local optimum, thus the efficiency of image segmentation is improved.