[1]杨晓兰,强彦,赵涓涓,等.基于医学征象和卷积神经网络的肺结节CT图像哈希检索[J].智能系统学报,2017,(06):857-864.[doi:10.11992/tis.201706035]
 YANG Xiaolan,QIANG Yan,ZHAO Juanjuan,et al.Hashing retrieval for CT images of pulmonary nodules based on medical signs and convolutional neural networks[J].CAAI Transactions on Intelligent Systems,2017,(06):857-864.[doi:10.11992/tis.201706035]
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基于医学征象和卷积神经网络的肺结节CT图像哈希检索(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2017年06期
页码:
857-864
栏目:
出版日期:
2017-12-25

文章信息/Info

Title:
Hashing retrieval for CT images of pulmonary nodules based on medical signs and convolutional neural networks
作者:
杨晓兰1 强彦1 赵涓涓1 杜晓平2 赵文婷1
1. 太原理工大学 计算机科学与技术学院, 山西 太原 030024;
2. 山西省煤炭中心医院 PET/CT中心, 山西 太原 030012
Author(s):
YANG Xiaolan1 QIANG Yan1 ZHAO Juanjuan1 DU Xiaoping2 ZHAO Wenting1
1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China;
2. PET/CT Center of Shanxi Coal Central Hospital, Taiyuan 030012, China
关键词:
肺结节医学征象卷积神经网络主成分分析语义特征哈希函数自适应图像检索
Keywords:
pulmonary nodulesmedical signsconvolutional neural networksprincipal components analysissemantic featuresHashing Functionadaptiveimage retrieval
分类号:
TP391
DOI:
10.11992/tis.201706035
摘要:
针对肺结节图像检索中存在的两个问题:手工设计的特征对肺结节的表达能力不强,生成的哈希码检索效果不佳。文中提出一种基于医学征象和卷积神经网络的肺结节CT图像哈希检索方法。首先,依据肺结节的9种征象取值,构造训练集准确的哈希码;其次,利用卷积神经网络和主成分分析法提取肺结节的重要语义特征,并结合训练集准确的哈希码反向求解哈希函数;最后,提出一种基于自适应比特位的检索方法,实现待查询肺结节图像的快速检出。通过对数据集进行实验和分析,证实了本文方法在肺结节图像检索过程中取得了较高的准确率和检索精度。
Abstract:
Existing pulmonary nodule retrieval methods have two problems; it is difficult to express the characteristics of pulmonary nodules using hand-crafted features and the generated hashing codes have poor retrieval performance. To address these issues, this paper proposes a retrieval method for pulmonary nodules in CT images based on medical signs and convolutional neural networks. We first constructed accurate hashing codes using an accurate training set based on the values of the nine signs of pulmonary nodules. We then extracted the important semantic features of pulmonary nodules using convolutional neural networks and principal components analysis. In addition, we inversely solved the hashing functions by combining the hashing codes with the accurate training set. Finally, we developed a retrieval method, based on adaptive bits, to achieve fast searching for pulmonary nodule images. Extensive experiments and evaluations on data sets show that the method has high accuracy and retrieval precision in the process of pulmonary nodule image retrieval.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-06-13;改回日期:。
基金项目:国家自然科学基金项目(61373100);虚拟现实技术与系统国家重点实验室开放基金项目(BUAA-VR-17KF-14,BUAA-VR-17KF-15);山西省回国留学人员科研项目(2016-038).
作者简介:杨晓兰,女,1991年生,硕士研究生,主要研究方向为图像处理与图像检索;强彦,男,1969年生,教授,博士生导师,博士,主要研究方向为图像处理、云计算、大数据。主持参与国家自然科学基金、虚拟现实技术与系统国家重点实验室开放基金等项目;赵涓涓,女,1975年生,教授,博士生导师,博士,主要研究方向为图像处理、模式识别、深度学习。主持参与国家自然科学基金、山西省回国留学人员科研资助项目等项目。
通讯作者:杨晓兰.E-mail:1141183481@qq.com.
更新日期/Last Update: 2018-01-03