An Accurate and Practical Explicit Hybrid Method for the Chan–Vese Image Segmentation Model

Darae Jeong,Junseok Kim,Chaeyoung Lee,Sangkwon Kim

doi:10.3390/math8071173

Darae Jeong, Junseok Kim + Show 2 more

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Journal: Mathematics	Publication Date: Jul 17, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: Kangwon National University, Korea University

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In this paper, we propose a computationally fast and accurate explicit hybrid method for image segmentation. By using a gradient flow, the governing equation is derived from a phase-field model to minimize the Chan–Vese functional for image segmentation. The resulting governing equation is the Allen–Cahn equation with a nonlinear fidelity term. We numerically solve the equation by employing an operator splitting method. We use two closed-form solutions and one explicit Euler’s method, which has a mild time step constraint. However, the proposed scheme has the merits of simplicity and versatility for arbitrary computational domains. We present computational experiments demonstrating the efficiency of the proposed method on real and synthetic images.

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Image segmentation is a process of partitioning an image into some non-intersecting regions [1,2].Image segmentation is important in many computer vision and image processing applications [3].For example, we need to segment MR brain images to get a 3D brain image
We presented a computationally fast and accurate explicit hybrid method for image segmentation
By using a gradient flow, the governing equation is derived from a phase-field model to minimize the Chan–Vese functional for image segmentation

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Image segmentation is a process of partitioning an image into some non-intersecting regions [1,2].Image segmentation is important in many computer vision and image processing applications [3].For example, we need to segment MR brain images to get a 3D brain image. Image segmentation is a process of partitioning an image into some non-intersecting regions [1,2]. Image segmentation is important in many computer vision and image processing applications [3]. We need to segment MR brain images to get a 3D brain image. One of the most widely used methods for binary image segmentation is Chan and Vese model [5], which is based on the level set method [6]. Shi and Pan proposed a local and global binary fitting model using the variational level set approach [8]. Çataloluk and Çelebi implemented an image segmentation algorithm based Chan–Vese algorithm [9].

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