基于SECC的医学图像自动标注与检索

Jian Yao, Sameer Kiran Antani, L. Long, G. Thoma, Zhongfei Zhang
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引用次数: 19

摘要

对自动注释和检索医学图像的需求比以往任何时候都增长得更快。本文提出了一种基于语义纠错输出码(SECC)的医学图像标注方法。利用这种标注方法,我们提出了一种新的利用高水平语义相似度的语义图像检索方法。例如,用户可能使用手臂的图像查询系统,而他/她期望的是手的图像。这是传统检索方法无法实现的。在IMAGECLEF 2005标注数据集上的实验结果清楚地表明了所提方法的有效性和应用前景
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Medical Image Annotation and Retrieval Using SECC
The demand for automatically annotating and retrieving medical images is growing faster than ever. In this paper, we present a novel medical image annotation method based on the proposed semantic error-correcting output codes (SECC). With this annotation method, we present a new semantic image retrieval method, which exploits the high level semantic similarity. For example, a user may query the system using an image of arm while he/she expects images of hand. This cannot be realized by traditional retrieval methods. The experimental results on the IMAGECLEF 2005 annotation data set clearly show the strength and the promise of the presented methods
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