基于有监督哈希的肺结节CT图像检索

摘要

针对传统方法在面对大量肺部数据时检索效率不高的问题,提出了一种基于有监督哈希的肺结节CT图像检索方法.通过图像预处理建立肺结节图像库,并从灰度、形态、纹理方面提取图像多特征;利用监督信息构造哈希函数,将多特征映射为低维哈希码;根据设计的自适应权重计算图像相似度,并返回相似的肺结节图像.实验结果表明,该方法能有效地实现肺结节CT图像的快速检索,对查询病灶的良恶性分类精度达到了89.45％.%In order to improve the retrieval efficiency when facing a large number of lung images,this paper presented a retrieval method for lung nodules CT image based on supervised hashing.The method firstly built lung nodules image database through image pre-procession and extracted the multi-features from gray,morphology and texture.Then,it utilized supervised information to construct hash functions and translated the multi-features into short hash codes.Finally,it retrieved the similar lung nodules CT images according to similarity calculation with adaptive weight.Experimental results show that the proposed method can effectively achieve the rapid retrieval of lung nodules CT image and the classification of benign and malignant tumor can reach 89.45％.