AMSLS: Adaptive multi-scale level set method based on local entropy for image segmentation

Intensity inhomogeneity often appears in medical images and causes great difficulties in image segmentation. Most active contour models perform poorly when applied to intensity inhomogeneous images because their energy functions use local intensity information in a fixed-size domain, causing the contour to evolve in the wrong direction. To overcome the difficulties caused by intensity inhomogeneity, we propose an adaptive multi-scale Gaussian kernel function based on image local entropy, which can determine the appropriate scale for each local region. Choosing the small scale and large scale for inhomogeneous and homogeneous areas respectively make the contour move toward the target boundary accurately. We also propose three adaptive multi-scale (AMS) models, AMS-region scalable fitting (AMS-RSF) model, AMS-local image fitting (AMS-LIF) model, AMS-local and global intensity fitting (AMS-LGIF) model, to segment medical images with intensity inhomogeneity and noise, including left atrial MR images and breast ultrasound images. The experimental results show that the adaptive multi-scale Gaussian kernel function enables the active contour model to effectively segment intensity inhomogeneous images and has a certain robustness to the initial contour and noise, which achieves good performance on MR left atrial images and ultrasound images of breast cancer. The AMS-LGIF model obtained the highest DICE coefficient of 0.9532, which was better than the 0.9429 obtained by the second-ranked LGIF model to segment left atrial MR images. For segmenting breast ultrasound images, the DICE coefficient is increased by 16% than that of the U-Net++ model.