School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China.
J Healthc Eng. 2023 Feb 17;2023:4387134. doi: 10.1155/2023/4387134. eCollection 2023.
In recent years, brain magnetic resonance imaging (MRI) image segmentation has drawn considerable attention. MRI image segmentation result provides a basis for medical diagnosis. The segmentation result influences the clinical treatment directly. Nevertheless, MRI images have shortcomings such as noise and the inhomogeneity of grayscale. The performance of traditional segmentation algorithms still needs further improvement. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. First, we introduce multitask learning strategy into FCM to extract public information among different segmentation tasks. It combines the advantages of the two algorithms. The algorithm enables to utilize both public information among different tasks and individual information within tasks. Then, we design an adaptive task weight learning mechanism, and a weighted multitask fuzzy C-means (WMT-FCM) clustering algorithm is proposed. Under the adaptive task weight learning mechanism, each task obtains the optimal weight and achieves better clustering performance. Simulated MRI images from McConnell BrainWeb have been used to evaluate the proposed algorithm. Experimental results demonstrate that the proposed method provides more accurate and stable segmentation results than its competitors on the MRI images with various noise and intensity inhomogeneity.
近年来，脑磁共振成像（MRI）图像分割引起了广泛关注。MRI 图像分割结果为医学诊断提供了依据，直接影响临床治疗效果。然而，MRI 图像存在噪声和灰度不均匀等缺点，传统的分割算法性能仍有待进一步提高。本文提出了一种基于模糊 C 均值（FCM）聚类算法的脑 MRI 图像分割新算法，以提高分割精度。首先，我们将多任务学习策略引入 FCM 中，以提取不同分割任务之间的公共信息。该方法结合了两种算法的优势，使算法能够同时利用不同任务之间的公共信息和任务内部的个体信息。然后，我们设计了一种自适应任务权重学习机制，并提出了一种加权多任务模糊 C 均值（WMT-FCM）聚类算法。在自适应任务权重学习机制下，每个任务都能获得最优权重，从而实现更好的聚类性能。使用 McConnell BrainWeb 的模拟 MRI 图像对所提出的算法进行了评估。实验结果表明，与其他竞争方法相比，该方法在具有各种噪声和强度不均匀性的 MRI 图像上提供了更准确和稳定的分割结果。
