基于有监督哈希的肺结节CT图像检索

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％。...

Full description

Saved in:
Bibliographic Details
Published in计算机应用研究 Vol. 34; no. 9; pp. 2838 - 2842
Main Author 潘玲 杜晓平 赵涓涓
Format Journal Article
LanguageChinese
Published 太原理工大学计算机科学与技术学院,山西晋中,030600%山西省煤炭中心医院PET-CT中心,太原,030006 2017
Subjects
分类
图像检索
多特征提取
有监督哈希
肺结节
自适应权重
multi-feature extraction
图像检索
supervised hashing
image retrieval
自适应权重
lung nodules
肺结节
adaptive weight
classification
多特征提取
有监督哈希
分类
Online AccessGet full text

Cover

More Information
Summary: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％。
Bibliography:51-1196/TP
In order to improve the retrieval efficiency when facing a large number of lung images, this paper presented a re- trieval 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%.
lung nodules; image retrieval ; multi-feature extraction ; supervised hashing ; adaptive weight ; classification
Pan Ling1, Du Xiaoping2, Zhao Juanjuan1. （ 1. College of Computer Science ＆ Technology, Taiyuan University of Technology, Jinzhong Shanx
ISSN:1001-3695
DOI:10.3969/j.issn.1001-3695.2017.09.061