School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
Department of Information Technology, Techno India College of Technology, West Bengal, India.
Curr Med Imaging. 2020;16(10):1214-1228. doi: 10.2174/1573405616666200218130043.
A huge amount of medical data is generated every second, and a significant percentage of the data are images that need to be analyzed and processed. One of the key challenges in this regard is the recovery of the data of medical images. The medical image recovery procedure should be done automatically by the computers that are the method of identifying object concepts and assigning homologous tags to them. To discover the hidden concepts in the medical images, the lowlevel characteristics should be used to achieve high-level concepts and that is a challenging task. In any specific case, it requires human involvement to determine the significance of the image. To allow machine-based reasoning on the medical evidence collected, the data must be accompanied by additional interpretive semantics; a change from a pure data-intensive methodology to a model of evidence rich in semantics. In this state-of-art, data tagging methods related to medical images are surveyed which is an important aspect for the recognition of a huge number of medical images. Different types of tags related to the medical image, prerequisites of medical data tagging, different techniques to develop medical image tags, different medical image tagging algorithms and different tools that are used to create the tags are discussed in this paper. The aim of this state-of-art paper is to produce a summary and a set of guidelines for using the tags for the identification of medical images and to identify the challenges and future research directions of tagging medical images.
每秒都会产生大量的医疗数据，其中很大一部分数据是需要进行分析和处理的图像。在这方面，一个关键的挑战是医疗图像数据的恢复。医疗图像的恢复过程应该由计算机自动完成，通过识别对象概念并为其分配同源标签的方法来实现。为了发现医疗图像中的隐藏概念，应该使用低级特征来实现高级概念，这是一项具有挑战性的任务。在任何特定情况下，都需要人类的参与来确定图像的重要性。为了允许机器对收集到的医疗证据进行推理，数据必须附有额外的解释语义；从纯粹的数据密集型方法转变为语义丰富的证据模型。在这篇综述中，调查了与医疗图像相关的数据标记方法，这是识别大量医疗图像的重要方面。本文讨论了与医疗图像相关的不同类型的标签、医疗数据标记的前提条件、开发医疗图像标签的不同技术、不同的医疗图像标记算法以及用于创建标签的不同工具。本文的目的是总结和制定一套使用标签来识别医疗图像的准则，并确定标记医疗图像的挑战和未来研究方向。
