On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.
On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.
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DOI：
10.1016/j.media.2014.06.009
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发表时间：
2014-10
影响因子：
10.9
通讯作者：
Rubin, Daniel L.
中科院分区：
文献类型：
--
作者：
Kurtz, Camille;Depeursinge, Adrien;Napel, Sandy;Beaulieu, Christopher F.;Rubin, Daniel L.
关键词：
计算机辅助图像检索应用程序可以通过识别档案中的相似图像来帮助放射科医生，作为提供决策支持的一种手段。在经典的情况下，使用从其内容中提取的低级特征来描述图像，并且使用适当的距离来在特征空间中找到最佳匹配。然而，使用低级别的图像特征来完全捕捉疾病的视觉外观是具有挑战性的，并且这些特征与放射学中的高级别视觉概念之间的语义差距可能会损害系统性能。为了处理这个问题，最近提倡使用语义术语来提供对放射图像内容的高级描述。然而，大多数现有的语义图像检索策略受到两个因素的限制：它们需要使用语义术语对图像进行手动注释，并且在图像的比较过程中忽略了这些注释之间的内在视觉和语义关系。基于这些考虑，我们提出了一个基于语义特征的图像检索框架，它依赖于两个主要策略：（1）描述来自多尺度Riesz小波的图像内容的本体术语的自动“软”预测，以及（2）通过使用新的术语相异性度量来评估它们的注释之间的相似性来检索相似图像，它同时考虑了基于图像的术语关系和本体论术语关系。这些策略的组合提供了一种基于图像注释在数据库中准确检索相似图像的方法，并且可以被认为是语义鸿沟问题的潜在解决方案。我们验证了这种方法的背景下，从计算机断层扫描（CT）图像检索肝脏病变，并标注RadLex本体的语义术语。检索结果的相关性进行了评估，使用两个协议：评价相对于一个相异性参考标准定义的图像对25图像数据集，并评价相对于诊断的检索图像的72图像数据集。第一个方案获得了超过0.92的归一化折扣累积增益（NDCG）评分，而第二个方案获得了超过0.77的AUC评分。这种自动化的方法可以通过向放射科医生显示具有相关诊断的类似图像以及对治疗的反应（如果可用），为他们提供实时决策支持。
Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies.
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影响因子：
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