Abstract
Efficient retrieval of relevant medical cases using semantically similar medical images from large scale repositories can assist medical experts in timely decision making and diagnosis. However, the ever-increasing volume of images hinder performance of image retrieval systems. Recently, features from deep convolutional neural networks (CNN) have yielded state-of-the-art performance in image retrieval. Further, locality sensitive hashing based approaches have become popular for their ability to allow efficient retrieval in large scale datasets. In this paper, we present a highly efficient method to compress selective convolutional features into sequence of bits using Fast Fourier Transform (FFT). Firstly, highly reactive convolutional feature maps from a pre-trained CNN are identified for medical images based on their neuronal responses using optimal subset selection algorithm. Then, layer-wise global mean activations of the selected feature maps are transformed into compact binary codes using binarization of its Fourier spectrum. The acquired hash codes are highly discriminative and can be obtained efficiently from the original feature vectors without any training. The proposed framework has been evaluated on two large datasets of radiology and endoscopy images. Experimental evaluations reveal that the proposed method significantly outperforms other features extraction and hashing schemes in both effectiveness and efficiency.
Similar content being viewed by others
References
Aborokbah, M.M., Al-Mutairi, S., Sangaiah, A.K., and Samuel, O.W., Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis. Sustain. Cities Soc., 2017. https://doi.org/10.1016/j.scs.2017.09.004.
Samuel, O.W., Asogbon, G.M., Sangaiah, A.K., Fang, P., and Li, G., An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst. Appl. 68:163–172, 2017.
Ahmad, J., Sajjad, M., Mehmood, I., and Baik, S.W., SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs. PloS One. 12(8):e0181707, 2017.
Ahmad, J., Sajjad, M., Mehmood, I., Rho, S., and Baik, S.W., Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems. J. Real-Time Image Proces. 13(3):431–447, 2017. https://doi.org/10.1007/s11554-015-0536-0.
Ahmad, J., Sajjad, M., Rho, S., and Baik, S.W., Multi-scale local structure patterns histogram for describing visual contents in social image retrieval systems. Multimed. Tools Appl. 75(20):12669–12692, 2016. https://doi.org/10.1007/s11042-016-3436-9.
Jégou, H., Douze, M., and Schmid, C., Improving bag-of-features for large scale image search. Int. J. Comput. Vis. 87(3):316–336, 2010.
Wang, J., Li, Y., Zhang, Y., Xie, H., and Wang, C. Boosted learning of visual word weighting factors for bag-of-features based medical image retrieval. In: Image and Graphics (ICIG), 2011 Sixth International Conference on, 2011. IEEE, pp 1035–1040
Lowe, D.G., Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2):91–110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94.
Dalal, N., and Triggs, B., Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE computer society conference on, 2005. IEEE, pp 886–893
Jégou, H., Douze, M., Schmid, C., and Pérez, P., Aggregating local descriptors into a compact image representation. In: Computer Vision and Pattern Recognition (CVPR), 2010 I.E. conference on, 2010. IEEE, pp 3304–3311
Douze, M., Ramisa, A., and Schmid, C., Combining attributes and fisher vectors for efficient image retrieval. In: in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. IEEE, pp 745–752
Liu, L., Shen, C., Wang, L., van den Hengel, A., and Wang, C., Encoding high dimensional local features by sparse coding based fisher vectors. In: Advances in neural information processing systems, 2014. pp 1143–1151
Oliva, A., and Torralba, A., Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3):145–175, 2001.
Wu, J., and Rehg, J.M., CENTRIST: A visual descriptor for scene categorization. IEEE Trans. Pattern Anal. Mach. Intell. 33(8):1489–1501, 2011.
Zhang, R., Shen, J., Wei, F., Li, X., and Sangaiah, A. K., Medical image classification based on multi-scale non-negative sparse coding. Artif. Intell. Med. 83:44–51, 2017. https://doi.org/10.1016/j.artmed.2017.05.006.
He, K., Zhang, X., Ren, S., and Sun, J., Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. pp 770–778
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A., Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015. pp 1–9
Girshick, R., Donahue, J., Darrell, T., and Malik, J., Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014. pp 580–587
Long, J., Shelhamer, E., and Darrell, T., Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015. pp 3431–3440
Babenko, A., Slesarev, A., Chigorin, A., and Lempitsky, V., Neural codes for image retrieval. In: Computer Vision–European Conference on Computer Vision (ECCV). Springer, 2014. pp 584–599. doi:https://doi.org/10.1007/978-3-319-10590-1_38
Babenko, A., and Lempitsky, V., Aggregating local deep features for image retrieval. In: Proceedings of the IEEE international conference on computer vision, 2015. pp 1269–1277
Ahmad, J., Mehmood, I., and Baik, S.W., Efficient object-based surveillance image search using spatial pooling of convolutional features. J. Vis. Commun. Image Represent. 45:62–76, 2017.
Ahmad, J., Mehmood, I., Rho, S., Chilamkurti, N., and Baik, S.W., Embedded deep vision in smart cameras for multi-view objects representation and retrieval. Comput. Electr Eng. 61C:297–311, 2017. https://doi.org/10.1016/j.compeleceng.2017.05.033.
Ahmad, J., Muhammad, K., and Baik, S.W., Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search. PloS One. 12(8):e0183838, 2017.
Krizhevsky, A., Sutskever, I., and Hinton, G. E., Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. Curran associates, Inc., pp 1097–1105, 2012.
Gong, Y., Wang, L., Guo, R., and Lazebnik, S., Multi-scale orderless pooling of deep convolutional activation features. In: Computer Vision–ECCV 2014. Springer, pp 392-407, 2014
Kalantidis, Y., Mellina, C., and Osindero, S., Cross-dimensional weighting for aggregated deep convolutional features. In: European Conference on Computer Vision, 2016. Springer, pp 685–701
Alzu’bi, A., Amira, A., and Ramzan, N., Content-based image retrieval with compact deep convolutional features. Neurocomputing 249:95-105, 2017. https://doi.org/10.1016/j.neucom.2017.03.072.
Razavian, A. S., Azizpour, H., Sullivan, J., and Carlsson, S., CNN Features Off-the-Shelf: An Astounding Baseline for Recognition. In: 2014 I.E. Conference on Computer Vision and Pattern Recognition Workshops, 23–28 June 2014 2014. pp 512–519. https://doi.org/10.1109/CVPRW.2014.131
Azizpour, H., Razavian, A., Sullivan, J., Maki, A., and Carlsson, S., From generic to specific deep representations for visual recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2015. pp 36–45.
Mohedano, E., McGuinness, K., O'Connor, N. E., Salvador, A., Marqués, F., and Giró-i-Nieto, X., Bags of local convolutional features for scalable instance search. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, 2016. ACM, pp 327–331
Srinivas, M., Naidu, R.R., Sastry, C., and Mohan, C.K., Content based medical image retrieval using dictionary learning. Neurocomputing. 168:880–895, 2015. https://doi.org/10.1016/j.neucom.2015.05.036.
Ahmad, J., Muhammad, K., Lee, M.Y., and Baik, S.W., Endoscopic image classification and retrieval using clustered convolutional features. J. Med. Syst. 41(12):196, 2017. https://doi.org/10.1007/s10916-017-0836-y.
Liao, X., Yin, J., Guo, S., Li, X., and Sangaiah, A.K., Medical JPEG image steganography based on preserving inter-block dependencies. Comput. Electr. Eng., 2017. https://doi.org/10.1016/j.compeleceng.2017.08.020.
Charikar, M. S., Similarity estimation techniques from rounding algorithms. In: Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, 2002. ACM, pp 380–388.
Weiss, Y., Torralba, A., and Fergus, R., Spectral hashing. In: Advances in neural information processing systems, 2009. pp 1753–1760.
Heo, J-P., Lee, Y., He, J., Chang, S-F., and Yoon, S-E., Spherical hashing. In: Computer Vision and Pattern Recognition (CVPR), 2012 I.E. conference on, 2012. IEEE, pp 2957–2964.
Kulis, B., and Grauman, K., Kernelized locality-sensitive hashing. IEEE Trans. Pattern Anal. Mach. Intell. 34(6):1092–1104, 2012. https://doi.org/10.1109/TPAMI.2011.219.
Jin, Z., Li, C., Lin, Y., and Cai, D., Density sensitive hashing. IEEE trans.cybern. 44(8):1362–1371, 2014.
Gong, Y., and Lazebnik, S., Iterative quantization: A procrustean approach to learning binary codes. In: Computer Vision and Pattern Recognition (CVPR), 2011 I.E. Conference on, 2011. IEEE, pp 817–824.
Yu, F., Kumar, S., Gong, Y., and Chang, S.- F., Circulant binary embedding. In: International conference on machine learning, 2014. pp 946–954.
Zhang, T., Du, C., and Wang, J., Composite Quantization for Approximate Nearest Neighbor Search. In: ICML, 2014. vol 2. pp 838–846
Erin Liong, V., Lu, J., Wang, G., Moulin, P., and Zhou, J., Deep hashing for compact binary codes learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015. pp 2475–2483
Lai, H., Pan, Y., Liu, Y., and Yan, S., Simultaneous feature learning and hash coding with deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015. pp 3270–3278.
Zhao, F., Huang, Y., Wang, L., and Tan, T., Deep semantic ranking based hashing for multi-label image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015. pp 1556–1564.
Deng, J., Dong, W., Socher, R., Li, L-J., Li, K., and Fei-Fei, L., Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 2009. IEEE, pp 248–255
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556
Welter, P., Deserno, T.M., Fischer, B., Günther, R.W., and Spreckelsen, C., Towards case-based medical learning in radiological decision making using content-based image retrieval. BMC Med. Inform. Decis. Mak. 11(1):1, 2011. https://doi.org/10.1186/1472-6947-11-68.
Pogorelov, K., Randel, K. R., Griwodz, C., Eskeland, S. L., de Lange, T., Johansen, D., Spampinato, C., Dang-Nguyen, D-T., Lux, M., and Schmidt, P. T., Kvasir: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection. In: Proceedings of the 8th ACM on Multimedia Systems Conference, 2017. ACM, pp 164–169.
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (No.2016R1A2B4011712).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that there is no conflict of interest.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
This article is part of the Topical Collection on Image & Signal Processing
Rights and permissions
About this article
Cite this article
Ahmad, J., Muhammad, K. & Baik, S.W. Medical Image Retrieval with Compact Binary Codes Generated in Frequency Domain Using Highly Reactive Convolutional Features. J Med Syst 42, 24 (2018). https://doi.org/10.1007/s10916-017-0875-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-017-0875-4