基于视觉信息与征象标签的肺结节CT图像检索

南京大学学报(自然科学版) ›› 2017, Vol. 53 ›› Issue (6): 1043–.

基于视觉信息与征象标签的肺结节CT图像检索

宋云霞1，强　彦1*，赵涓涓1，唐笑先2，田　奇3

出版日期:2017-11-26 发布日期:2017-11-26
作者简介:1.太原理工大学计算机科学与技术学院，太原，030024；
2.山西省人民医院CT室，太原，030012；
3．美国德克萨斯大学圣安东尼奥分校计算机科学系，圣安东尼奥，TX 78249，美国
基金资助:
基金项目：国家自然科学基金(61373100)，山西省回国留学人员科研资助项目(2016－038)，虚拟现实技术与系统国家重点实验室开放基金(BUAA－VR－17KF－14，BUAA－VR－17KF－15)
收稿日期：2017－10－28
*通讯联系人，E－mail：qiangyan99@foxmail.com

Pulmonary nodule CT images retrieval based on visual information and signs label

Song Yunxia1，Qiang Yan1*，Zhao Juanjuan1，Tang Xiaoxian2，Tian Qi3

Online:2017-11-26 Published:2017-11-26
About author:1.School of Computer Science and Technology，Taiyuan University of Technology，Taiyuan，030024，China；
2．Shanxi Provincial People’s Hospital，CT Room，Taiyuan，030012，China；
3．Department of Computer Science，University of Texas at San Antonio，San Antonio，TX 78249，USA

摘要/Abstract

摘要： 肺结节CT图像的相似性检索是计算机辅助诊断系统中最重要的部分，目前常用的检索方法通常匹配精度低，检索速度慢．针对上述问题，提出一种新的基于视觉信息与征象标签的双概率超图哈希算法，使用两层结构提高肺结节图像的检索精度：在第一层，将肺结节影像视觉信息和标签信息分别构建概率超图，最优划分概率超图得到哈希码；在第二层，使用结节图像的视觉特征、标签特征和第一层得到的哈希码来训练哈希函数．在检索时，对待检图像通过训练好的哈希函数进行0，1编码，与数据集中图像比较汉明距离，返回相似结节图像．对9种不同征象类型的3422张肺结节CT图像进行实验，并与不同哈希算法进行比较，结果表明，提出的方法在哈希码长为32位时可以达到最高精度90.18%，有效提高了检索精度，可以给医生提供客观的辅助诊断．

Abstract: The similarity search of CT images of pulmonary nodules is the most important part of computer aided diagnosis system.At present，commonly used retrieval methods have problems of low matching accuracy and slow retrieval speed.Aiming at these problems，we propose a new hashing algorithm used double probabilistic hypergraphs based on visual information and signs label，using two－layer structure to improve retrieval precision.On the first layer，we utilize visual information and label information to construct probabilistic hypergraph respectively，and hashing codes can be obtained by the optimal partition of probabilistic hypergraph.On the second layer，we use visual features，labels and hashing codes obtained from the first layer to train hashing functions.On retrieval stage，we encode the query image by training hashing functions into 0 or 1，comparing hamming distance with the database images，and returning similar nodule images.In this paper，experiments were performed on 3422 pulmonary nodule CT images with 9 different types of signs.Compared with different hashing algorithms，the results show that the proposed method can achieve higher accuracy of 90.18% when the length of hashing code is 32－bit，and effectively improves the retrieval accuracy，which can provide the doctors with objective auxiliary diagnosis.

宋云霞1，强　彦1*，赵涓涓1，唐笑先2，田　奇3. 基于视觉信息与征象标签的肺结节CT图像检索[J]. 南京大学学报(自然科学版), 2017, 53(6): 1043–.

Song Yunxia1，Qiang Yan1*，Zhao Juanjuan1，Tang Xiaoxian2，Tian Qi3. Pulmonary nodule CT images retrieval based on visual information and signs label[J]. Journal of Nanjing University(Natural Sciences), 2017, 53(6): 1043–.

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