Image Segmentation Based on Local Chan-Vese Model Combined with Fractional Order Derivative

Zou, Le; Song, Lixin; Chen, Yanping; Tang, Chao; Zhang, Chen

doi:10.1007/978-3-030-26763-6_32

Cited by 4 publications

(1 citation statement)

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“…Also, in [26], an effective algorithm for benign brain tumours detection has been developed using fractional calculus. Indeed, the fractional-order derivative helps in considering more neighbouring pixels information and extracting more image texture details, which encouraged more applications using this tool as well as in image enhancement [27][28][29][30], image segmentation [31,32], image registration [33,34], in-painting [35], and neural networks [36,37].…”

Section: Introductionmentioning

confidence: 99%

A bi‐directional fractional‐order derivative mask for image processing applications

2020

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Fractional computation has been recently designed as a major mathematical tool in image and signal processing fields. This study presents a novel operator established for two‐dimensional fractional differentiation. It is developed based on the one‐dimensional Charef fractional differentiation extension. A new multi‐directional mask is proposed and a new adaptive fractional‐order computation is introduced. The proposed method uses the gradient computation properties. It has been applied in edge detection and de‐noising problems using real and synthetic images. Obtained results have been compared to those given by integer and fractional useful operators. Results demonstrate that the fractional edge images obtained using the proposed operator has more complete and clear contour information and more abundant texture detail information. The performances have been improved by the proposed method.

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