Zhu Hong, He Hanzhi, Xu Jinhui, Fang Qianhao, Wang Wei
School of Medical Information, Xuzhou Medical University, Xuzhou, China.
Key Laboratory of Intelligent Industrial Control Technology of Jiangsu Province, College of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, China.
Comput Math Methods Med. 2018 Dec 24;2018:3052852. doi: 10.1155/2018/3052852. eCollection 2018.
In this paper, we propose a novel algorithm for medical image segmentation, which combines the density peaks clustering (DPC) with the fruit fly optimization algorithm, and it has the following advantages. Firstly, it avoids the problem of DPC that needs to artificially select parameters (such as the number of clusters) in its decision graph and thus can automatically determine their values. Secondly, our algorithm uses random step size, instead of the fixed step size as in the fruit fly optimization algorithm, which helps avoid falling into local optima. Thirdly, our algorithm selects the cut-off distance and the cluster centers using the image entropy value and can better capture the structures of the image. Experiments on benchmark dataset and proprietary dataset show that our algorithm can adaptively segment medical images with faster convergence and better robustness.
在本文中，我们提出了一种用于医学图像分割的新型算法，该算法将密度峰值聚类（DPC）与果蝇优化算法相结合，具有以下优点。首先，它避免了DPC在其决策图中需要人工选择参数（如聚类数量）的问题，从而能够自动确定这些参数的值。其次，我们的算法使用随机步长，而不是像果蝇优化算法那样使用固定步长，这有助于避免陷入局部最优。第三，我们的算法利用图像熵值来选择截止距离和聚类中心，能够更好地捕捉图像的结构。在基准数据集和专有数据集上的实验表明，我们的算法能够以更快的收敛速度和更好的鲁棒性对医学图像进行自适应分割。
