Sedighin Farnaz
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
J Med Signals Sens. 2024 Jul 10;14:16. doi: 10.4103/jmss.jmss_55_23. eCollection 2024.
In the past decade, tensors have become increasingly attractive in different aspects of signal and image processing areas. The main reason is the inefficiency of matrices in representing and analyzing multimodal and multidimensional datasets. Matrices cannot preserve the multidimensional correlation of elements in higher-order datasets and this highly reduces the effectiveness of matrix-based approaches in analyzing multidimensional datasets. Besides this, tensor-based approaches have demonstrated promising performances. These together, encouraged researchers to move from matrices to tensors. Among different signal and image processing applications, analyzing biomedical signals and images is of particular importance. This is due to the need for extracting accurate information from biomedical datasets which directly affects patient's health. In addition, in many cases, several datasets have been recorded simultaneously from a patient. A common example is recording electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) of a patient with schizophrenia. In such a situation, tensors seem to be among the most effective methods for the simultaneous exploitation of two (or more) datasets. Therefore, several tensor-based methods have been developed for analyzing biomedical datasets. Considering this reality, in this paper, we aim to have a comprehensive review on tensor-based methods in biomedical image analysis. The presented study and classification between different methods and applications can show the importance of tensors in biomedical image enhancement and open new ways for future studies.
在过去十年中，张量在信号与图像处理领域的不同方面越来越具有吸引力。主要原因是矩阵在表示和分析多模态及多维数据集方面效率低下。矩阵无法保留高阶数据集中元素的多维相关性，这极大地降低了基于矩阵的方法在分析多维数据集时的有效性。除此之外，基于张量的方法已展现出良好的性能。这些因素共同促使研究人员从矩阵转向张量。在不同的信号与图像处理应用中，分析生物医学信号和图像尤为重要。这是因为需要从生物医学数据集中提取准确信息，而这直接影响患者的健康。此外，在许多情况下，会同时从患者身上记录多个数据集。一个常见的例子是记录精神分裂症患者的脑电图（EEG）和功能磁共振成像（fMRI）。在这种情况下，张量似乎是同时利用两个（或更多）数据集的最有效方法之一。因此，已经开发了几种基于张量的方法来分析生物医学数据集。鉴于此现实，在本文中，我们旨在对生物医学图像分析中基于张量的方法进行全面综述。所呈现的研究以及不同方法与应用之间的分类可以表明张量在生物医学图像增强中的重要性，并为未来的研究开辟新途径。
