Department of Computer Science, Faculty of Computers & Artificial Intelligence, Benha University, Benha, Egypt.
Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, Egypt.
J Digit Imaging. 2023 Jun;36(3):1248-1261. doi: 10.1007/s10278-022-00769-7. Epub 2023 Jan 26.
Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively.
基于内容的医学图像检索系统变得越来越重要，尤其是随着医学成像技术的进步和医学图像数据库的增长。此外，这些系统还可以利用医学图像更好地掌握和深入了解不同疾病的原因和治疗方法，而不仅仅是用于诊断。为了实现所有这些目的，非常需要一种高效准确的基于内容的医学图像检索（CBMIR）方法。本文提出了一种用于计算机断层扫描（CT）和磁共振（MR）图像检索的高效方法（RbQE）。RbQE 基于扩展查询的特征，并利用预先训练的学习模型 AlexNet 和 VGG-19 从医学图像中提取紧凑、深度和高级特征。RbQE 有两种搜索过程：快速搜索和最终搜索。在快速搜索中，通过从每个类别中检索排名最高的图像来扩展原始查询，并通过计算排名最高的图像的深度特征的平均值来重新构造查询，从而为每个类别生成一个新查询。在最终搜索中，将使用与原始查询最相似的新查询从数据库中进行检索。将所提出的方法的性能与四个公开可用的标准数据库（TCIA-CT、EXACT09-CT、NEMA-CT 和 OASIS-MRI）上的最新方法进行了比较。实验结果表明，所提出的方法在 TCIA-CT、EXACT09-CT、NEMA-CT 和 OASIS-MRI 数据库中的平均检索精度（ARP）分别比比较方法高 0.84%、4.86%、1.24%和 14.34%。
