An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation
An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation
复制标题
用于非均匀图像分割和偏置场估计的自适应尺度主动轮廓模型
DOI：
10.1016/j.patcog.2018.05.008
复制
发表时间：
2018-10-01
影响因子：
8
通讯作者：
Li, Jing
中科院分区：
文献类型：
--
作者：
Cai, Qing;Liu, Huiying;Li, Jing
活动轮廓模型是一种应用广泛的图像分割方法。现有的活动轮廓模型在处理强度不均匀性严重的图像时，表现不佳。为了解决这一问题，我们提出了一种基于图像熵和半朴素贝叶斯分类器的自适应尺度活动轮廓模型（ASACM），该模型可以对具有严重强度不均匀性的图像同时进行分割和偏置场估计。首先，构造自适应尺度算子，根据强度非均匀性的程度自适应调整ASACM的尺度；其次，我们定义了一个改进的偏置场估计项，通过对每个像素分配相关隶属函数来估计严重非均匀图像中的偏置场。第三，利用分段多项式提出了一种新的惩罚项，避免了传统惩罚项耗时的重新初始化过程和不稳定性。实验结果表明，所提出的ASACM在分割精度、分割效率和鲁棒性（w.r.t初始化和噪声）方面始终优于许多最先进的模型。(C) 2018 Elsevier Ltd.版权所有。
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. (C) 2018 Elsevier Ltd. All rights reserved.