Accurate segmentation of brain tissues in magnetic resonance imaging (MRI) data plays critical role in the clinical diagnostic and treatment planning. The presence of noise and artifacts in MRI data degrades the performance of segmentation algorithms. In this view, the present study proposes a complete unsupervised clustering based multi-objective modified fuzzy c-mean (MOFCM) segmentation algorithm, which inculcates multi-objective antlion optimization (MOALO) to minimize the cluster compactness and fuzzy hyper-volume fitness functions. The output segmented image corresponds to minimum value of partition entropy in the obtained solution set. The present study integrates proposed MOFCM with a new cluster number validity index, which allows user not to provide number of segments in image as an input. The proposed MOFCM algorithm is extensively validated on seventy two synthetic images corrupted with different levels of Gaussian, Speckle and Rician noises, forty simulated BrainWeb MRI images suffered from noise and inhomogeneity, and 10 real IBSR MRI dataset of images. The results are compared with existing popular clustering based algorithms, and supervised deep learning based algorithms, i.e. UNet, SegNet and QuickNAT. The proposed MOFCM algorithm demonstrate the superior segmentation performance in comparison to popular FCM based clustering algorithms, SegNet and UNet, whereas the segmentation results of proposed MOFCM are at par with QuickNAT.

磁共振成像（MRI）数据中脑组织的准确分割在临床诊断和治疗计划中起着至关重要的作用。MRI数据中噪声和伪影的存在会降低分割算法的性能。有鉴于此，本研究提出了一种完全无监督的基于聚类的多目标改进模糊c均值（MOFCM）分割算法，该算法灌输了多目标蚁群优化（MOALO）以最小化聚类的紧凑性和模糊超量适应度函数。输出的分割图像对应于所获得的解集中的分区熵的最小值。本研究将提出的MOFCM与新的簇数有效性指数集成在一起，该指数允许用户不提供图像中的片段数作为输入。所提出的MOFCM算法已在具有不同水平的高斯噪声，斑点噪声和里奇噪声的72个合成图像，40个受噪声和不均匀性影响的模拟BrainWeb MRI图像以及10个真实IBSR MRI图像数据集上得到了广泛验证。将结果与现有流行的基于聚类的算法以及受监督的基于深度学习的算法（即UNet，SegNet和QuickNAT）进行比较。与流行的基于FCM的聚类算法SegNet和UNet相比，提出的MOFCM算法展示了优越的分割性能，而提出的MOFCM的分割结果与QuickNAT相当。和10个真实的IBSR MRI图像数据集。将结果与现有流行的基于聚类的算法以及受监督的基于深度学习的算法（即UNet，SegNet和QuickNAT）进行比较。与流行的基于FCM的聚类算法SegNet和UNet相比，提出的MOFCM算法展示了优越的分割性能，而提出的MOFCM的分割结果与QuickNAT相当。和10个真实的IBSR MRI图像数据集。将结果与现有流行的基于聚类的算法以及受监督的基于深度学习的算法（即UNet，SegNet和QuickNAT）进行比较。与流行的基于FCM的聚类算法SegNet和UNet相比，提出的MOFCM算法展示了优越的分割性能，而提出的MOFCM的分割结果与QuickNAT相当。