An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation. (October 2018)

• Record Type: Journal Article
• Authors: Cai, Qing; Liu, Huiying; Zhou, Sanping; Sun, Jingfeng; Li, Jing
• Is Part Of: Pattern recognition. Volume 82(2018:Oct.)
• Page Start: 79
• Page End: 93
• Publication Date: 2018-10
• Subjects: Active contour model -- Image segmentation -- Intensity inhomogeneous image -- Adaptive scale operator -- Bias field estimation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.05.008
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. Abstract: The active contour model is a widely used method for image segmentation. Most existing active contour models yield poor performance when applied to images with severe intensity inhomogeneity. To address this issue, we propose an adaptive-scale active contour model (ASACM) based on image entropy and semi-naive Bayesian classifier, which achieves simultaneous segmentation and bias field estimation for images with severe intensity inhomogeneity. Firstly, an adaptive scale operator is constructed to adaptively adjust the scale of the ASACM according to the degree of the intensity inhomogeneity. Secondly, we define an improved bias field estimation term via distributing a dependent-membership function for each pixel to estimate the bias field in severe inhomogeneous images. Thirdly, a new penalty term is proposed using piecewise polynomial, which helps to avoid time-consuming re-initialization process and instability in conventional penalty term. The experimental results demonstrate that the proposed ASACM consistently outperforms many state-of-the-art models in segmentation accuracy, segmentation efficiency and robustness w.r.t initialization and noise.