Rahman Md Mahmudur, Antani Sameer K, Demner-Fushman Dina, Thoma George R
Morgan State University , Computer Science Department, Calloway 308, 1700 E Cold Spring Lane, Baltimore, Maryland 21251, United States.
U.S. National Library of Medicine , National Institutes of Health, 8600 Rockville Pike, Bethesda, Maryland 20894, United States.
J Med Imaging (Bellingham). 2015 Oct;2(4):046502. doi: 10.1117/1.JMI.2.4.046502. Epub 2015 Dec 30.
This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term "concept" refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature.
本文提出了一种生物医学图像检索方法，该方法通过将图像区域映射到局部概念，其中图像在基于加权熵的概念特征空间中进行表示。术语“概念”指的是在图像区域中局部识别出的可感知区分的视觉块，并且可以映射到成像术语词汇表。此外，概念的视觉显著性（例如，可视性）被测量为图像块中像素值的香农熵，并用于细化特征向量。而且，该系统可以帮助用户交互式地选择感兴趣区域（ROI）并搜索相似的图像ROI。此外，空间验证步骤用作后处理步骤，以基于位置信息改善检索结果。通过对从开放获取生物医学文献中收集的两个不同数据集进行实验，验证了此类方法将改善生物医学图像检索的假设。
