Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409-0040, USA.
Comput Med Imaging Graph. 2011 Jul;35(5):365-72. doi: 10.1016/j.compmedimag.2010.11.008. Epub 2010 Dec 8.
The illustrations in biomedical publications often provide useful information in aiding clinicians' decisions when full text searching is performed to find evidence in support of a clinical decision. In this research, image analysis and classification techniques are explored to automatically extract information for differentiating specific modalities to characterize illustrations in biomedical publications, which may assist in the evidence finding process. Global, histogram-based, and texture image illustration features were compared to basis function luminance histogram correlation features for modality-based discrimination over a set of 742 manually annotated images by modality (radiological, photo, etc.) selected from the 2004-2005 issues of the British Journal of Oral and Maxillofacial Surgery. Using a mean shifting supervised clustering technique, automatic modality-based discrimination results as high as 95.57% were obtained using the basis function features. These results compared favorably to other feature categories examined. The experimental results show that image-based features, particularly correlation-based features, can provide useful modality discrimination information.
生物医学出版物中的插图通常在进行全文搜索以寻找支持临床决策的证据时提供有用的信息。在这项研究中，探索了图像分析和分类技术，以自动提取信息，区分特定模式，对生物医学出版物中的插图进行特征描述，这可能有助于证据发现过程。通过对从 2004-2005 年《英国口腔颌面外科杂志》中选择的 742 张手动注释图像按模式（放射学、照片等）进行分类，比较了基于全局、直方图和纹理的图像插图特征与基于基函数亮度直方图相关的特征，用于基于模式的区分。使用均值漂移监督聚类技术，使用基函数特征可获得高达 95.57%的自动基于模式的区分结果。这些结果与其他检查的特征类别相比表现良好。实验结果表明，基于图像的特征，特别是基于相关的特征，可以提供有用的模式区分信息。
