Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (1): 326-336.doi: 10.23940/ijpe.19.01.p33.326336
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Pinle Qinab, Qi Liab, Jianchao Zengab*(
), Haiyan Liuc, and Yuhao Cuia
Revised on
;
Accepted on
Contact:
Zeng Jianchao
E-mail:zjc@nuc.edu.cn
About author:Pinle Qin received the PhD degree in computer application technology from Dalian University of Technology (DLUT), Dalian, Liaoning, P.R. China, in 2008. He is currently an associate professor with the School of Data Science and Technology, North University of China (NUC). His current research interests include computer vision, medical image processing and deep learning.|Qi Li is pursing Master degree from NUC, and his areas of interest are digital image processing, medical image processing and computer vision.|Jianchao Zeng received the PhD degree from Xi’an Jiaotong University, Xi’an, Shanxi, P.R. China, in 1990. He is currently the vice president of North University of China (NUC). His current research interests include computer vision, medical image processing and deep learning.|Haiyan Liu Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China; Molecular Imaging Precision Medical Collaborative InnovationCenter, Shanxi Medical University, Taiyuan, Shanxi 030001, China.|Yuhao Cui born in 1996, B.S. candidate. His research interests include deep learning, digital image processing.
Pinle Qin, Qi Li, Jianchao Zeng, Haiyan Liu, and Yuhao Cui. Fully Convolutional-based Dense Network forLungNodule Image Retrieval Algorithm [J]. Int J Performability Eng, 2019, 15(1): 326-336.
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Figure 1
a. (a)Origin;(b) Binary; (c) Clear-border; (d) Closing; (e) Dilation; (f) Input;b. Lung nodule samples"
Figure 2
Medical image retrieval principle"
Figure 3
The structure of network"
Figure 4
The dense block"
Figure 5
a. Layer 4 retrieval top5results;b. Layer 17 retrieval top5 results;c. Layer 25 retrieval top5 results"
Figure 6
Network middle layer retrieval results feature comparison"
Figure 7
a. FCDN network shallow layers of image retrieval top5 distance comparison;b. FCDN network deep layers of image retrieval top5 distance comparison"
Figure 8
Combine distance retrievaltop5 result"
Table 1
Other methods of different bit MAP (%) value"
| Method | 24bit 32 bit 48 bit 64 bit |
|---|---|
| ours | 76.1 78.3 86.7 91.7 |
| ABR | 69.6 74.8 78.7 88.2 |
| KSL | 66.2 70.8 74.5 75.9 |
| ITQ | 55.9 66.7 72.5 76.3 |
| SH | 26.4 33.4 36.1 35.8 |
| LSH | 10.3 18.2 29.1 36.5 |
Figure 9
Comparisons of precision and recall of lung nodule retrieval"
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