? 基于语义特征和有监督哈希的图像检索的肺结节医学征象识别
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (3) :457-469    DOI: 10.1007/s11390-017-1736-9
Special Section of CVM 2017 << Previous Articles | Next Articles >>
基于语义特征和有监督哈希的图像检索的肺结节医学征象识别
Juan-Juan Zhao1, Member, CCF, Ling Pan1, Peng-Fei Zhao1, Xiao-Xian Tang2
1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China;
2. Shanxi Provincial People's Hospital, Taiyuan 030012, China
Medical Sign Recognition of Lung Nodules based on Image Retrieval with Semantic Feature and Supervised Hashing
Juan-Juan Zhao1, Member, CCF, Ling Pan1, Peng-Fei Zhao1, Xiao-Xian Tang2
1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China;
2. Shanxi Provincial People's Hospital, Taiyuan 030012, China

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摘要 计算机辅助诊断(CAD)有助于提高医师诊断肺部病灶的效率,特别是结节征象,对医师诊断病灶的良恶性具有重要作用。本文提出一种基于图像检索的肺结节医学征象的识别方法。首先,构建深度学习框架,提取表示肺结节征象的语义特征;其次利用主成分分析(PCA)和有监督的哈希方法将提取到的高维图像特征映射为简洁的二值码;接着根据提出的自适应加权的相似性计算方法检索具有相同征象的结节图像;最后从检索出的相似结节图像中识别查询图像的征象,并为医师诊断病灶提供决策支持。我们通过公共数据集LIDC-IDRI和LISS证实本文方法的性能。实验结果表明,本文方法确实提高了结节图像的检索效果,可以达到87.29%的检索精度,在检索结果的基础上识别肺结节医学征象的总体准确率为93.52%。此外,在临床诊断数据中的识别效果也十分有效。
关键词肺结节   医学征象识别   图像检索   有监督哈希   自适应权重     
Abstract: Computer-aided diagnosis (CAD) technology can improve the efficiency of a physician diagnosing lung lesions, especially sign recognition, which is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to extract semantic features that can effectively represent sign information. Then, we translate the high-dimensional image features into compact binary codes with principal component analysis (PCA) and supervised hashing. Next, we retrieve similar lung nodule images with the presented adaptive-weighted similarity calculation method. Finally, we recognize nodule signs from the retrieval results, which can also provide decision support for diagnosis of lung lesions. The proposed method is validated on the publicly available databases Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Lung Computed Tomography (CT) Imaging Signs (LISS). The experimental results demonstrate our method substantially improves retrieval performance and can achieve 87.29%. The entire recognition rate on the basis of the retrieval results can achieve 93.52%. Moreover, our method is also effective for real-life diagnosis data.
Keywordslung nodule   medical sign recognition   image retrieval   supervised hashing   adaptive weight     
Received 2016-12-27;
本文基金:

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373100, the Virtual Reality Technology and Systems National Key Laboratory of Open Foundation of China under Grant No. BUAA-VR-16KF-13 and the Shanxi Scholarship Council of China under Grant No. 2016-038.

About author: Juan-Juan Zhao is a professor in the College of Computer Science and Technology at Taiyuan University of Technology (TYUT), Taiyuan. She received her Ph.D. degree in computer application technology from TYUT, Taiyuan, in 2010. Her current research topics include medical image processing and the Internet of Things.
引用本文:   
Juan-Juan Zhao, Ling Pan, Peng-Fei Zhao, Xiao-Xian Tang.基于语义特征和有监督哈希的图像检索的肺结节医学征象识别[J]  Journal of Computer Science and Technology , 2017,V32(3): 457-469
Juan-Juan Zhao, Ling Pan, Peng-Fei Zhao, Xiao-Xian Tang.Medical Sign Recognition of Lung Nodules based on Image Retrieval with Semantic Feature and Supervised Hashing[J]  Journal of Computer Science and Technology, 2017,V32(3): 457-469
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