A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations.
A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations.
复制标题
一种基于分层的知识方法，用于检索用语义注释描述的类似医学图像。
DOI：
10.1016/j.jbi.2014.02.018
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发表时间：
2014-06
影响因子：
4.5
通讯作者：
Rubin, Daniel L.
中科院分区：
文献类型：
--
作者：
Kurtz, Camille;Beaulieu, Christopher F.;Napel, Sandy;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大型档案中的相似图像来辅助放射科医生解释，作为提供决策支持的一种手段。然而，底层图像特征和它们的高层语义之间的语义鸿沟可能会影响系统的性能。事实上，它可以是具有挑战性的全面表征的图像使用低级别的成像特征，以充分捕捉图像上的疾病的视觉外观，最近的语义术语的使用已经提倡提供图像的视觉内容的语义描述。然而，大多数现有的图像检索策略不考虑这些条款的内在属性在比较的图像，除了把它们作为简单的二进制（存在/不存在）功能。我们提出了一个新的框架，包括图像中的语义特征，并使检索相似的图像在大型数据库中的语义关系的基础上。它基于两个主要步骤：（1）从本体中提取的语义术语的图像的注释，以及（2）通过使用层次语义距离（HSBD）耦合到本体测量计算术语之间的相似性来评估图像对的相似性。这两个步骤的组合提供了一种捕获用于表征图像的术语之间的语义相关性的方法，其可以被认为是处理语义差距问题的潜在解决方案。我们验证这种方法的检索和分类的背景下，从计算机断层扫描（CT）图像的肝脏提取的二维感兴趣区域（ROI）。在此框架下，检索精度超过0.96，获得了30个图像数据集使用归一化贴现累积增益（NDCG）指数，这是一个标准的技术，用于衡量信息检索算法的有效性时，一个单独的参考标准。在77张图像数据集上获得了超过95%的分类结果。为了比较的目的，使用的地球移动器的距离（EMD），这是一个替代的距离度量，考虑所有现有的条款之间的关系，导致结果检索精度为0.95和分类结果的93%，具有较高的计算成本。所提供的框架的结果是具有竞争力的国家的最先进的，并强调放射学图像检索和分类的建议方法的有用性。
Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification.
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影响因子：
10.9
作者：
Kwitt, R.;Vasconcelos, N.;Rasiwasia, N.;Uhl, A.;Davis, B.;Haefner, M.;Wrba, F.
通讯作者：
Wrba, F.
影响因子：
4.4
作者：
Korenblum, Daniel;Rubin, Daniel;Beaulieu, Chris
通讯作者：
Beaulieu, Chris
影响因子：
7.3
作者：
Ma, Hao;Zhu, Jianke;King, Irwin
通讯作者：
King, Irwin
影响因子：
3.6
作者：
Aigrain, P;Zhang, HJ;Petkovic, D
通讯作者：
Petkovic, D
DOI：
10.1136/amiajnl-2012-001380
发表时间：
2013-11-01
影响因子：
6.4
作者：
Chen, Yang;Ren, Xiaofeng;Xu, Rong
通讯作者：
Xu, Rong