Active contour driven by adaptive‐scale local‐energy signed pressure force function based on bias correction for medical image segmentation. Issue 14 (12th August 2022)

• Record Type: Journal Article
• Authors: Liu, Huaxiang; Fu, Youyao; Zhang, Shiqing; Liu, Jun; Fang, Jiangxiong
• Is Part Of: IET image processing. Volume 16:Issue 14(2022)
• Page Start: 3929
• Page End: 3947
• Publication Date: 2022-08-12
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12604
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: This paper presents an active contour model driven by an adaptive‐scale local‐energy signed pressure force (ALSPF) function based on bias correction for segmenting medical images with intensity inhomogeneity. Firstly, a local energy‐driven signed pressure force (LESPF) function is designed as the driving force to extract local image features, which can effectively deal with intensity inhomogeneity. Secondly, to avid selecting the neighbourhood size in the LESPF function, a multi‐scale adaptive selection schema is put forward to adaptively choose the local window size according to the degree of intensity inhomogeneity. Finally, a novel single‐well potential function proved to be a strictly monotonic function is designed to ensure the stable movement of the neighbourhood points in the vicinity of the evolution curve, which can not only avoid the re‐initialization process but also enhance. Experiments on synthetic and medical images demonstrate that the proposed model is more robust than the popular ACMs for segmenting images with intensity inhomogeneity. The code is available at the website: https://github.com/HuaxiangLiu/ALSPF