[1]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,2017,12(6):857-864.[doi:10.11992/tis.201706035]
Copy
Hashing retrieval for CT images of pulmonary nodules based on medical signs and convolutional neural networks
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 6
Page number:
857-864
Column:
学术论文—机器学习
Public date:
2017-12-25
- Title:
- Hashing retrieval for CT images of pulmonary nodules based on medical signs and convolutional neural networks
- Keywords:
- pulmonary nodules; medical signs; convolutional neural networks; principal components analysis; semantic features; Hashing Function; adaptive; image retrieval
- CLC:
- TP391
- DOI:
- 10.11992/tis.201706035
- 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.
- References:
-
[1] REBECCA L, KIMBERLY D M, STACEY A F, et al. Cancer statistics[J]. CA: a cancer journal for clinicians, 2017, 67 (3): 177.
[2] ZHAO Y, BOCK G H D, VLIEGENTHART R, et al. Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume[J]. European radiology, 2012, 22(10): 2076.
[3] CHEN S, NI D, QIN J, et al. Bridging computational features toward multiple semantic features with multi-task regression: a study of CT pulmonary nodules[M]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016. Springer International Publishing, 2016: 53-60.
[4] VADLAMUDI L N, VADDELLA R P V, DEVARA V. Robust image hashing technique for content authentication based on DWT[C]//Proceedings of International Conference on Computer Vision and Image Processing. Singapore: Springer, 2017: 189-191.
[5] GIONIS A, ⅡNDYK P, MOTWANI R. Similarity search in high dimensions via Hashing[C]//International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc. 2000: 518-529.
[6] WEISS Y, TORRALBA A, FERGUS R. Spectral Hashing [J]. Proc nips, 2008, 282(3): 1753-1760.
[7] GONG Y, LAZEBNIK S, GORDO A, et al. Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(12): 2916-29.
[8] LIU W, WANG J, JI R, et al. Supervised hashing with kernels[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, USA, 2012: 2074-2081.
[9] JING M, ZHANG S, HUANG J, et al. Joint kernel-based supervised hashing for scalable histopathological image analysis[C]//Medical Image Computing and Computer-Assisted Intervention 2015. Springer International Publishing, 2015, 1: 558-560.
[10] LIU J, ZHANG S, LIU W, et al. Scalable mammogram retrieval using composite anchor graph hashing with iterative quantization[J]. IEEE transactions on circuits and systems for video technology, 2016(99): 1-1.
[11] LIU H M, WANG R P, SHAN S, et al. Deep supervised hashing for fast image retrieval[C]//Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA, 2016: 2064-2072.
[12] YANG H F, LIN K, CHEN C S. Supervised learning of semantics-preserving hash via deep convolutional neural networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2015(99): 1-1.
[13] ARMATO S, MCLENNAN G, MCNITTt-GRAY M, et al. WEB201B02: the lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed public database of CT scans for lung nodule analysis[J], Medical physics, 2010, 37(6): 3416-3417.
[14] YANG X, CHENG K T. Local difference binary for ultrafast and distinctive feature description[J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 36(1): 188-94.
[15] TARANDO S R, FETITA C. Increasing CAD system efficacy for lung texture analysis using a convolutional network[C]//Proceedings of SPIE 9785, Medical Imaging. San Diego, USA, 2016: 97850Q.
[16] BABENKO, SLESAREV A, CHIGORIN A, et al. Neural codes for image retrieval[M]. Springer International Publishing, 2014: 584-599.
[17] FU H, KONG X, WANG Z. Binary code reranking method with weighted hamming distance[J]. Multimedia tools and applications, 2016, 75(3): 1391-1408.
[18] 王超,王浩,王伟,等. 基于优化ROI的医学图像分割与压缩方法研究[J]. 重庆邮电大学学报: 自然科学版, 2015, 27(2): 279-284.
WANG Chao, WANG Hao, WANG Wei, et al. Study of optimized ROI based medical image segmentation and compression method[J]. Journal of Chongqing university of posts and telecommunications: natural science edition, 2015, 27(2): 279-284.
[19] LIAO X, ZHAO J, CHENG J, et al. A segmentation method for lung parenchyma image sequences based on superpixels and a self-generating neural forest[J]. Plos one, 2016, 11(8): e0160556.
- Similar References:
Memo
-
Last Update:
2018-01-03