Title: Image segmentation using active contour model driven by RSF and difference of Gaussian energy

Authors: Qile Zhang; Xiaoliang Jiang

Addresses: Rehabilitation Department, Quzhou People's Hospital, Quzhou, 324000, China ' College of Mechanical Engineering, Quzhou University, Quzhou, 324000, China; College of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

Abstract: As we all know, the region scalable fitting method is sensitive to initialisations and suffers from bad results in images with complex scene. In our article, we put forward a new framework by integrating region scalable fitting (RSF) term and difference of Gaussian (DOG) term for segmenting images. We first propose a DOG function which can enhance the contrast at the edges of objects. Then, the RSF energy term is introduced to drive the curve closer to the edge. In the next step, the regularisation term is established which can avoid of the process of reinitialisation. Compared with traditional classical methods, the proposed technique is more flexibility with initialisation and has better segmentation performance.

Keywords: difference of Gaussian; DOG; region scalable fitting; RSF; active contour; image segmentation.

DOI: 10.1504/IJICT.2022.121782

International Journal of Information and Communication Technology, 2022 Vol.20 No.3, pp.270 - 278

Received: 29 Jun 2020
Accepted: 25 Aug 2020
Published online: 07 Apr 2022 *

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