Cai Qing, Qian Yiming, Zhou Sanping, Li Jinxing, Yang Yee-Hong, Wu Feng, Zhang David
IEEE Trans Image Process. 2022;31:43-57. doi: 10.1109/TIP.2021.3127848. Epub 2021 Nov 24.
Intensity inhomogeneity and noise are two common issues in images but inevitably lead to significant challenges for image segmentation and is particularly pronounced when the two issues simultaneously appear in one image. As a result, most existing level set models yield poor performance when applied to this images. To this end, this paper proposes a novel hybrid level set model, named adaptive variational level set model (AVLSM) by integrating an adaptive scale bias field correction term and a denoising term into one level set framework, which can simultaneously correct the severe inhomogeneous intensity and denoise in segmentation. Specifically, an adaptive scale bias field correction term is first defined to correct the severe inhomogeneous intensity by adaptively adjusting the scale according to the degree of intensity inhomogeneity while segmentation. More importantly, the proposed adaptive scale truncation function in the term is model-agnostic, which can be applied to most off-the-shelf models and improves their performance for image segmentation with severe intensity inhomogeneity. Then, a denoising energy term is constructed based on the variational model, which can remove not only common additive noise but also multiplicative noise often occurred in medical image during segmentation. Finally, by integrating the two proposed energy terms into a variational level set framework, the AVLSM is proposed. The experimental results on synthetic and real images demonstrate the superiority of AVLSM over most state-of-the-art level set models in terms of accuracy, robustness and running time.
强度不均匀性和噪声是图像中的两个常见问题，但不可避免地给图像分割带来重大挑战，当这两个问题同时出现在一幅图像中时尤为明显。因此，大多数现有的水平集模型应用于这类图像时性能较差。为此，本文提出了一种新颖的混合水平集模型，通过将自适应尺度偏差场校正项和去噪项集成到一个水平集框架中，命名为自适应变分水平集模型（AVLSM），该模型可以在分割过程中同时校正严重的强度不均匀性并进行去噪。具体来说，首先定义一个自适应尺度偏差场校正项，通过在分割时根据强度不均匀程度自适应调整尺度来校正严重的强度不均匀性。更重要的是，该术语中提出的自适应尺度截断函数与模型无关，可以应用于大多数现成的模型，并提高它们在处理具有严重强度不均匀性的图像分割时的性能。然后，基于变分模型构建一个去噪能量项，它不仅可以去除常见的加性噪声，还可以去除分割过程中医学图像中经常出现的乘性噪声。最后，通过将这两个提出的能量项集成到一个变分水平集框架中，提出了AVLSM。在合成图像和真实图像上的实验结果表明，AVLSM在准确性、鲁棒性和运行时间方面优于大多数最先进的水平集模型。
