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.

中文翻译：

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AMSLS：基于局部熵的自适应多尺度水平集图像分割方法


医学图像中经常出现强度不均匀性，给图像分割带来很大困难。大多数主动轮廓模型在应用于强度不均匀图像时表现不佳，因为它们的能量函数使用固定大小域中的局部强度信息，导致轮廓向错误的方向演化。为了克服强度不均匀性带来的困难，我们提出了一种基于图像局部熵的自适应多尺度高斯核函数，它可以确定每个局部区域的适当尺度。对于非均匀区域和均匀区域分别选择小比例尺和大比例尺，使轮廓准确地向目标边界移动。我们还提出了三种自适应多尺度（AMS）模型，AMS-区域可扩展拟合（AMS-RSF）模型，AMS-局部图像拟合（AMS-LIF）模型，AMS-局部和全局强度拟合（AMS-LGIF）模型，分割具有强度不均匀性和噪声的医学图像，包括左心房 MR 图像和乳房超声图像。实验结果表明，自适应多尺度高斯核函数使得主动轮廓模型能够有效分割强度不均匀图像，并且对初始轮廓和噪声具有一定的鲁棒性，在MR左心房图像和乳腺超声图像上取得了良好的性能。癌症。 AMS-LGIF模型获得最高的DICE系数0.9532，优于排名第二的LGIF模型分割左心房MR图像获得的0.9429。对于乳腺超声图像分割，DICE系数比U-Net++模型提高了16%。