Fuzzy Active Contour Model Using Fractional-Order Diffusion Based Edge Indicator and Fuzzy Local Fitted Image

Hongli Lv,Renfang Wang,Fangjian Zhang

doi:10.1109/access.2020.3025570

Abstract

Local region-based active contour models (ACMs) can effectively segment images corrupted by intensity inhomogeneity, however, they always converge to local minimum and are sensitive to the initial position of contour. In this paper, a novel fuzzy ACM is proposed to tackle these problems. In order to deal with intensity inhomogeneity, the fuzzy local fitted image is first defined and utilized for constructing a local-region based fuzzy energy term, which is minimized in a variational level set framework to accurately segment inhomogeneous images. Second, the fractional-order diffusion based edge indicator is used to scale the local fuzzy energy term to reduce the effect of intensity inhomogeneity. Third, the fuzzy signed pressure force (FSPF) function defined by local image information is used for constructing the weighted area term to further improve the accuracy of the developed model. Finally, the global FSPF is formulated and used as an adaptive force, which can drive the level set function (LSF) to adaptively move up or down according to image intensity information. Therefore, the initial contour can be initialized as a constant function, which eliminates the problem caused by contour initialization. Moreover, the global FSPF makes the proposed model not easy to fall into local minimum. The results of experiments on synthetic and real images validate the accuracy of the proposed model for inhomogeneous image segmentation.

Highlights

Image segmentation is a primordial task in image analysis and computer vision
This experiment demonstrates that the defined global fuzzy signed pressure force (FSPF) spfG(I (x)) can drive the pseudo level set function u(x) to automatically move up or down, which allows u(x) to be initialized as a constant function
The corresponding fuzzy local fitted images are shown in row 1 of Fig. 4, which demonstrate that a large number of the undesired background information of the original images is greatly suppressed

Summary

Introduction

Image segmentation is a primordial task in image analysis and computer vision. The main goal of image segmentation is to partition an image into non-intersected regions with approximately similar property, such as texture, color, intensity, etc [1]. Many methods have been proposed for image segmentation. The basic idea of the ACMs is to state the problem of image segmentation as the minimization of an energy functional. Level set based ACMs implicitly represent an evolving curve as the zero level set of a higher dimension function [3], [4], and the initialized curve is driven by a partial differential equation, which is obtained by minimizing a predefined energy

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Results

Discussion

Conclusion