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Content-Based Image Retrieval in Radiology: Current Status and Future Directions

机译：放射学中基于内容的图像检索：现状和未来方向

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Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.
机译：诊断放射学要求准确解释医学图像中的复杂信号。基于内容的图像检索（CBIR）技术对于放射线医师通过识别大型档案中的相似图像（可能有助于决策支持）来评估医学图像时可能非常有价值。 CBIR已经取得了许多进步，并且非医学领域中出现了各种系统。然而，这些方法在放射学中的渗透受到限制。我们在这篇综述中的目标是从放射学应用的角度调查CBIR方法和系统，并确定在非医学应用中开发的可以转化为放射学的方法。与消费领域的图像相比，放射学图像提出了特定的挑战。它们包含变化，丰富且通常微妙的功能，在评估图像相似性时需要认识到这些功能。放射学图像也为CBIR提供了丰富的机会：放射学家提供了有关图像语义的丰富元数据，并且此信息尚未在CBIR系统中得到最大利用。通过集成基于像素和基于元数据的图像特征分析，可以实现医学CBIR的实质性进步，CBIR系统成为放射学实践中的重要工具。

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