烟花算法优化的软子空间MR图像聚类算法

现有的软子空间聚类算法在分割MR图像时易受随机噪声的影响，而且算法因依赖于初始聚类中心的选择而容易陷入局部最优，导致分割效果不理想.针对这一问题，提出一种基于烟花算法的软子空间MR图像聚类算法.算法首先设计一个结合界约束与噪声聚类的目标函数，弥补现有算法对噪声数据敏感的缺陷，并提出一种隶属度计算方法，快速、准确地寻找簇类所在子空间；然后，在聚类过程中引入自适应烟花算法，有效地平衡局部与全局搜索，弥补现有算法容易陷入局部最优的不足.EWKM，FWKM，FSC，LAC算法在UCI数据集、人工合成图像、Berkeley图像数据集以及临床乳腺MR图像、脑部MR图像上的聚类结果表明，所提出的算法不仅在UCI数据集上能够取得较好的结果，而且对图像聚类也具有较好的抗噪性能，尤其是对MR图像的聚类具有较高的精度和鲁棒性，能够较为有效地实现MR图像的分割.

Soft Subspace Algorithm for MR Image Clustering Based on Fireworks Optimization Algorithm

The existing soft subspace clustering algorithm is susceptible to random noise when MR images are segmented, and it is easy to fall into local optimum due to the choice of the initial clustering centers, which leads to unsatisfactory segmentation results. To solve these problems, this paper proposes a soft subspace algorithm for MR image clustering based on fireworks algorithm. Firstly, a new objective function with boundary constraints and noise clustering is designed to overcome the shortcomings of the existing algorithms that are sensitive to noise data. Next, a new method of calculating affiliation degree is proposed to find the subspace where the cluster is located quickly and accurately. Then, adaptive fireworks algorithm is introduced in the clustering process to effectively balance the local and global search, overcoming the disadvantage of falling into local optimum in the existing algorithms. Comparing with EWKM, FWKM,FSC and LAC algorithms, experiments are conducted on UCI datasets, synthetic images, Berkeley image datasets, as well as clinical breast MR images and brain MR images. The results demonstrate that the proposed algorithm not only can get better results on UCI datasets, but also has better anti-noise performance. Especially for MR images, high clustering precision and robustness can be obtained, and effective MR images segmentation can be achieved.