Image Retrieval Algorithm of Pulmonary Nodules Based on Similarity Measurement

被引：4
|
作者：
Wei G.-H. [1 ,2 ]
Qi S.-L. [1 ]
Qian W. [1 ]
Zhang K.-X. [2 ]
机构：
[1] School of Sino-Dutch Biomedical & Information Engineering, Northeastern University, Shenyang
[2] School of Science and Engineering, Shandong University of Traditional Chinese Medicine, Jinan
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2018年 / 39卷 / 09期
关键词：
Distance metric learning; Lung cancer; Medical image retrieval; Similarity measurement; Texture features;
D O I：
10.12068/j.issn.1005-3026.2018.09.003
中图分类号：
学科分类号：
摘要：
In order to overcome the shortcomings that CT of pulmonary lesions is complex and is very easy to lead to misdiagnosis, a medical image retrieval algorithm based on similarity measurement was proposed to diagnose lung cancer. The similarity measurement maintains the semantic relevance and visual similarity of the image. Firstly, a distance metric learning algorithm was constructed to learn a Mahalanobis distance on the basis of the proposed similarity measurement. Secondly, a novel medical image retrieval algorithm was proposed based on the learned distance metric to diagnose lung cancer. The study results demonstrate the feasibility and effectiveness of the proposed retrieval algorithm in lung cancer diagnosis. © 2018, Editorial Department of Journal of Northeastern University. All right reserved.
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收藏
页码：1226 / 1231
页数：5
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