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Integrating an automatic classification method into the medical image retrieval process

机译:将自动分类方法集成到医学图像检索过程中

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摘要

Combining low-level features that represent the content of medical images with high level features that are saved with images would allow the expansion of text queries submitted to Content Based Image Retrieval (CBIR) systems. Expanding these text queries would allow CBIR systems to respond more effectively to specific queries when retrieving medical images. We hypothesized that adding an automatic classification method to the current retrieval process would help improve the performance of the University at Buffalo Medical Text and Images Retrieval System (UBMedTIRS). This paper illustrates the results of our approach and its implications for expanding query statements in medical image information retrieval (IR) systems.
机译:将代表医学图像内容的低级特征与随图像保存的高级别特征相结合,将可以扩展提交给基于内容的图像检索(CBIR)系统的文本查询。扩展这些文本查询将使CBIR系统在检索医学图像时可以更有效地响应特定查询。我们假设在当前检索过程中添加自动分类方法将有助于提高布法罗大学医学文本和图像检索系统(UBMedTIRS)的性能。本文说明了我们方法的结果及其对扩展医学图像信息检索(IR)系统中的查询语句的含义。

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