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首页> 外文期刊>BMC Medical Genomics >Categorizing biomedicine images using novel image features and sparse coding representation
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Categorizing biomedicine images using novel image features and sparse coding representation

机译：使用新颖的图像特征和稀疏编码表示对生物医学图像进行分类

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Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [ 1 ]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance. Conclusions Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study.
机译：生物医学出版物中嵌入的背景图像带有丰富的信息，这些信息通常简明地总结了所采用的关键假设，所采用的方法或已发表研究中获得的结果。因此，它们为理解生物医学出版物的主要内容提供了宝贵的线索。先前的研究指出了挖掘嵌入生物医学出版物中的图像以自动理解和检索此类图像的相关原始文档的潜力。在生物医学图像处理的广泛领域中，对生物医学图像进行分类是构建许多高级图像分析，检索和挖掘应用程序的基本步骤。与任何自动分类工作类似，区分图像功能可以在此过程中提供最关键的帮助。方法我们观察到，嵌入生物医学出版物中的许多图像带有通用注释文本。因此，基于图像中这些文本元素的位置和它们之间的空间关系，我们提出了一些新颖的图像特征进行图像分类，以定量表征生物医学图像中文本元素的空间位置和分布。我们进一步采用基于稀疏编码表示（SCR）的技术，以利用我们新提出的图像特征对嵌入生物医学出版物中的图像进行分类。结果我们随机选择了990张JPG格式的图像用于我们的实验，其中310张图像用作训练样本，其余的用作测试用例。我们首先按照建议的程序对310个样本图像进行了分割。此步骤总共产生1035个子图像。然后，我们根据[1]提出的两级分层图像分类法，手动标记所有这些子图像。在我们的注释结果中，316是显微镜图像，126是凝胶电泳图像，135是折线图，156是条形图，52是点图，25是表，70是流程图，其余155图像是“其他”。获得了一系列实验结果。首先，给出每个图像的分类结果，然后列出所有图像分类的性能指标，如精度，召回率，F分数。包括常规图像特征和我们提出的新颖特征在内的不同特征表明了不同的分类性能，并证明了结果。第三，我们在支持向量机分类方法和我们提出的稀疏表示分类方法之间进行了准确性比较。最后，将我们提出的方法与三种同级分类方法进行了比较，实验结果证明了我们令人印象深刻的改进性能。结论与我们的比较基准研究中所证明的相比，与不利用生物医学出版物中嵌入的图像内部文本位置和分布特征的常规图像特征相比，我们提出的图像特征与基于SR的表示模型相结合具有更好的生物医学图像分类性能。

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