Feng Yuncong, Liu Wanru, Zhang Xiaoli, Liu Zhicheng, Liu Yunfei, Wang Guishen
College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
Artificial Intelligence Research Institute, Changchun University of Technology, Changchun 130012, China.
Entropy (Basel). 2021 Oct 29;23(11):1429. doi: 10.3390/e23111429.
In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid - layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.
在本文中，我们提出了一种区间迭代多级阈值法（IIMT）。该方法基于大津法，但通过迭代搜索图像的子区域来实现分割，而不是将整个图像作为一个整体区域进行处理。然后，提出了一种基于IIMT的用于脑磁共振图像分割的新型多级阈值框架。在该框架中，首先使用混合 - 层分解方法对原始图像进行分解以获得基底层。其次，我们使用IIMT对原始图像及其基底层进行分割。最后，通过融合方案将两个分割结果进行整合，以获得更精细、准确的分割结果。实验结果表明，我们提出的算法是有效的，并且优于基于标准大津法和其他基于优化的分割方法。
