Clustering is an important issue in brain medical image segmentation. Original medical images used for clinical diagnosis are often insufficient for clustering in the current domain. As there are sufficient medical images in the related domains, transfer clustering can improve the clustering performance of the current domain by transferring knowledge across the related domains. In this article, we propose a novel shared hidden space transfer fuzzy c- means (FCM) clustering called SHST-FCM for cross-domain brain computed tomography (CT) image segmentation. SHST-FCM projects both the data samples of the source domain and target domain into the shared hidden space, such that the distributions of the two domains are as close as possible. In the learned shared subspace, the data samples of the source domain serve as the auxiliary knowledge to aid the clustering process in the target domain. Extensive experiments on brain CT medical image datasets indicate the effectiveness of the proposed method.

中文翻译：

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基于共享隐藏空间转移FCM聚类的跨域脑CT图像智能分割

聚类是脑医学图像分割中的一个重要问题。用于临床诊断的原始医学图像通常不足以在当前领域进行聚类。由于相关域中有足够的医学图像，迁移聚类可以通过跨相关域迁移知识来提高当前域的聚类性能。在本文中，我们提出了一种新颖的共享隐藏空间转移模糊C-均值 (FCM) 聚类称为 SHST-FCM，用于跨域脑计算机断层扫描 (CT) 图像分割。SHST-FCM 将源域和目标域的数据样本都投影到共享隐藏空间中，使两个域的分布尽可能接近。在学习到的共享子空间中，源域的数据样本作为辅助知识来辅助目标域的聚类过程。对脑 CT 医学图像数据集的大量实验表明了该方法的有效性。