Di Ruberto Cecilia, Loddo Andrea, Putzu Lorenzo
Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy.
Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, 09123 Cagliari, Italy.
J Imaging. 2023 Mar 17;9(3):70. doi: 10.3390/jimaging9030070.
Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques.
医学成像广泛应用于诊断以及术后或治疗后监测。所产生的图像数量不断增加，这促使人们引入自动化方法来辅助医生或病理学家。近年来，尤其是卷积神经网络出现之后，许多研究人员都专注于这种方法，认为它是诊断的唯一方法，因为它可以对图像进行直接分类。然而，许多诊断系统仍然依赖手工特征来提高可解释性并限制资源消耗。在这项工作中，我们将精力集中在正交矩上，首先对其宏观类别进行概述和分类，然后分析它们在由四个公共基准数据集代表的非常不同的医学任务上的分类性能。结果证实，卷积神经网络在所有任务上都取得了优异的性能。尽管正交矩所包含的特征比网络提取的特征少得多，但事实证明它们与网络具有竞争力，表现出相当的性能，在某些情况下甚至更好。此外，笛卡尔矩和谐波矩类别具有非常低的标准差，证明了它们在医学诊断任务中的稳健性。考虑到所获得的性能和结果的低变异性，我们坚信所研究的正交矩的整合可以带来更稳健、更可靠的诊断系统。最后，由于它们已被证明在磁共振成像和计算机断层扫描图像上均有效，因此可以轻松扩展到其他成像技术。
