Top-k Medical Images Query Based on Association Graph
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摘要: 找到与病人具有相似纹理特征的医学图像，有助于医生结合历史病历信息对病人作出更为准确的诊断.基于此，大量的研究工作围绕如何提高基于内容的医学图像检索技术的准确性展开.然而，现有的基于内容的医学图像检索技术均是基于查询图像与数据库中图像的逐张匹配过程，面对迅速增长的医学图像数量，查询等待时间过长成为医学图像检索领域的另一主要问题.鉴于用户往往只对前k(Top-k)个检索结果感兴趣，提出了一种基于关联图模型的医学图像Top-k查询方法.首先，提出一种关联图模型，使用该模型可以有效地刻画医学图像之间关联关系的模糊性；继而利用关联图模型，提出一系列关联性度量计算方法，从而使得仅需对图像匹配一次即可更新所有图像与查询图像之间的相似度范围.由此，提出Top-k查询方法以及基于游走的查询优化策略.实验证明提出的方法可以有效地减少图像匹配次数，降低时间复杂度.Abstract: Patient-to-patient comparison, especially image-to-image comparison plays an important role in the medical domain since doctors invariably make diagnoses based on prior experiences of similar cases. It is very significant for doctors to find similar medical images from the database as similar pathological changes in prior patients’ images and corresponding reports can assist doctors to make diagnoses for current patients. Therefore, advanced medical image retrieval techniques have been widely studied to improve the accuracy in recent years. However, the processing time has become another problem in medical image retrieval domain because of the increasing number of medical images. As doctors are only interested in the most similar k results, a novel model of association graph is proposed for medical image top-k query in this paper. The fuzzy expression in a association graph can describe the similarity between images effectively. Moreover, a series of correlation measurements are proposed for similarity reasoning. Then the medical image top-k query method is represented based on the characters of correlation measurements. Furthermore, four walk strategies are studied to accelerate and stabilize the top-k process. Experimental results show that its efficiency and effectiveness are higher in comparison with state of the art.
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Keywords:
- association graph /
- Top-k query /
- walk strategy /
- image retrieval /
- medical image
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