Ontology of Gaps in Content-Based Image Retrieval
Ontology of Gaps in Content-Based Image Retrieval
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DOI：
10.1007/s10278-007-9092-x
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发表时间：
2009-04
影响因子：
4.4
通讯作者：
T. Deserno;Sameer Kiran Antani;L. Long
中科院分区：
文献类型：
--
作者：
T. Deserno;Sameer Kiran Antani;L. Long
基于内容的图像检索（CBIR）是一种很有前途的技术，可以丰富图像存档和通信系统（PACS）的核心功能。CBIR具有在诊断、研究和教育方面产生强大影响的潜力。然而，科学文献中报道的研究并没有取得重大进展，因为医学CBIR应用程序被纳入常规临床医学或医学研究。原因通常被归因于（没有支持分析）这些应用程序无法克服“语义差距”。语义鸿沟将具有人类认知能力的高级场景理解和解释与基于数学处理和人工智能方法的计算机的低级像素分析区分开来。在本文中，我们提出了一个更系统和全面的观点的概念，“差距”在医学CBIR研究。特别是，我们定义了一个本体的14个差距，解决了图像的内容和功能，以及系统的性能和可用性。除了这些差距，我们确定了七个系统特性，影响CBIR的适用性和性能。我们已经创建的框架，可以用来比较医疗CBIR系统和方法的具体生物医学图像领域和目标和先验的设计阶段的医疗CBIR应用程序，作为系统的差距分析提供了详细的见解系统比较，并有助于指导未来的研究。
Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the inability of these applications in overcoming the “semantic gap.” The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing and artificial intelligence methods. In this paper, we suggest a more systematic and comprehensive view of the concept of “gaps” in medical CBIR research. In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability. In addition to these gaps, we identify seven system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application, as the systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.