Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

Yıl: 2021 Cilt: 34 Sayı: 3 Sayfa Aralığı: 733 - 746 Metin Dili: İngilizce DOI: 10.35378/gujs.710730 İndeks Tarihi: 07-11-2022

Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

Öz:
It is very pleasing for human health that medical knowledge has increased and the technological infrastructure improves medical systems. The widespread use of medical imaging devices has been instrumental in saving lives by allowing early diagnosis of many diseases. These medical images are stored in large databases for many purposes. These datasets are used when a suspicious diagnostic case is encountered or to gain experience for inexperienced radiologists. To fulfill these tasks, images similar to one query image are searched from within the large dataset. Accuracy and speed are vital for this process, which is called content-based image retrieval (CBIR). In the literature, the best way to perform a CBIR system is by using hash codes. This study provides an effective hash code generation method based on feature selection-based downsampling of deep features extracted from medical images. Firstly, pre-hash codes of 256-bit length for each image are generated using a pairwise siamese network architecture that works based on the similarity of two images. Having a pre-hash code between -1 and 1 makes it very easy to generate hash code in hashing algorithms. For this reason, all activation functions of the proposed convolutional neural network (CNN) architecture are selected as hyperbolic tanh. Finally, neighborhood component analysis (NCA) feature selection methods are used to convert pre-hash code to binary hash code. This also downsamples the hash code length to 32-bit, 64-bit, or 96-bit levels. The performance of the proposed method is evaluated using NEMA MRI and NEMA CT datasets.
Anahtar Kelime: Cbmir Cnn Retrieval Hashing

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] Nour, M., Sindi, H., Abozinadah, E., Öztürk, Ş., Polat, K., “A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity”, International Journal of Medical Informatics, 144: 104300, (2020).
  • [2] Font, M. M., “Clinical applications of nuclear medicine in the diagnosis and evaluation of musculoskeletal sports injuries”, Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), 39(2): 112-34, (2020).
  • [3] Öztürk, Ş., “Stacked auto-encoder based tagging with deep features for content-based medical image retrieval”, Expert Systems with Applications, 161: 113693, (2020).
  • [4] Alsmadi, M. K., “Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features”, Arabian Journal for Science and Engineering, 45: 3317–3330, (2020).
  • [5] Jiang, Q. Y., Cui, X., Li, W. J., “Deep discrete supervised hashing”, IEEE Transactions on Image Processing, 27(12): 5996-6009, (2018).
  • [6] Jianhua, X., Adali, T., Yue, W., “Segmentation of magnetic resonance brain image: integrating region growing and edge detection”, Proceedings International Conference on Image Processing, 544-547, (1995).
  • [7] Choi, H., Baraniuk, R. G., “Multiscale image segmentation using wavelet-domain hidden Markov models”, IEEE Transactions on Image Processing, 10(9): 1309-1321, (2001).
  • [8] Zhang, G., Ma, Z. M., Tong, Q., He, Y., Zhao, T., “Shape Feature Extraction Using Fourier Descriptors with Brightness in Content-Based Medical Image Retrieval”, 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 71-4, (2008).
  • [9] Chandra, P. N. R. L. C., Prasad, P. S., Kumar, M. V., Santosh, D. H. H., “Image retrieval with rotation invariance”, 2011 3rd International Conference on Electronics Computer Technology, 194-198, (2011).
  • [10] Jai-Andaloussi, S., Lamard, M., Cazuguel, G., Tairi, H., Meknassi, M., Cochener, B., “Content based Medical Image Retrieval: use of Generalized Gaussian Density to model BEMD’s IMF”, World Congress on Medical Physics and Biomedical Engineering, 1249-1252, (2009).
  • [11] Ramamurthy, B., Chandran, K. R., Meenakshi, V. R., Shilpa, V., “CBMIR: Content Based Medical Image Retrieval System Using Texture and Intensity for Dental Images”, Eco-friendly Computing and Communication Systems, 125-134, (2012).
  • [12] Babaie, M., Tizhoosh, H. R., Khatami, A., Shiri, M. E., “Local radon descriptors for image search”, 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 1-5, (2017).
  • [13] Öztürk, Ş., “Image Inpainting based Compact Hash Code Learning using Modified U-Net”, In 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT2020), 1-5, (2020).
  • [14] Beura, S., Majhi, B., Dash, R., “Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer”, Neurocomputing, 154: 1-14, (2015).
  • [15] Banerji, S., Sinha, A., Liu, C., “A New Bag of Words LBP (BoWL) Descriptor for Scene Image Classification”, Computer Analysis of Images and Patterns, 490-497, (2013).
  • [16] Hadjiiski, L. M., Tourassi, G. D., Sadek, I., Sidibé, D., Meriaudeau, F., “Automatic discrimination of color retinal images using the bag of words approach”, Medical Imaging 2015: Computer-Aided Diagnosis, 9414, (2015).
  • [17] Vetrivel, A., Gerke, M., Kerle, N., Vosselman, G., “Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach”, Remote Sensing, 8: 1-22, (2016).
  • [18] Yamashita, R., Nishio, M., Do, R. K. G., Togashi, K., “Convolutional neural networks: an overview and application in radiology”, Insights into imaging, 9(4): 611-629, (2018).
  • [19] Bressan, R. S., Alves, D. H. A., Valerio, L. M., Bugatti, P. H., Saito, P. T. M., “DOCToR: The Role of Deep Features in Content-Based Mammographic Image Retrieval”, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 158-163, (2018).
  • [20] Bootwala, A., Breininger, K., Maier, A., Christlein, V., “Assistive Diagnosis in Opthalmology Using Deep Learning-Based Image Retrieval”, Bildverarbeitung für die Medizin, 144-149, (2020).
  • [21] Kumar, M., Singh, K. M., “Content based medical image retrieval system (CBMIRS) to diagnose hepatobiliary images”, In International Conference on Next Generation Computing Technologies, 663- 676, (2017).
  • [22] Cai, Y., Li, Y., Qiu, C., Ma, J., Gao, X., “Medical image retrieval based on convolutional neural network and supervised hashing”, IEEE Access. 7: 51877-51885, (2019).
  • [23] Chung, Y. A., Weng, W. H., “Learning deep representations of medical images using siamese CNNs with application to content-based image retrieval”, arXiv preprint arXiv: 1711.08490, (2017).
  • [24] Ayyachamy, S., Alex, V., Khened, M., Krishnamurthi, G., “Medical image retrieval using Resnet-18”, In Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 1095410, (2019).
  • [25] Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N. I., “Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review”, Mathematical Problems in Engineering, 1-21, (2019).
  • [26] Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A., “Return of the devil in the details: Delving deep into convolutional nets”, arXiv preprint arXiv: 1405.3531, (2014).
  • [27] Öztürk, Ş., Özkaya, U., Barstuğan, M., “Classification of Coronavirus (COVID 19) from X ray and CT images using shrunken features”, International Journal of Imaging Systems and Technology. (2020).
  • [28] Lehmann, T. M., Güld, M. O., Thies, C., Fischer, B., Spitzer, K., Keysers, D., Wein, B. B., “Content- based image retrieval in medical applications”, Methods of information in medicine. 43(04): 354-361, (2004).
  • [29] Cao, Y., Qi, H., Gui, J., Li, K., Tang, Y. Y., Kwok, J. T. Y., “Learning to Hash with a Dimension Analysis-based Quantizer for Image Retrieval”, IEEE Transactions on Multimedia, (2020).
APA ÖZTÜRK Ş (2021). Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. , 733 - 746. 10.35378/gujs.710730
Chicago ÖZTÜRK Şaban Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. (2021): 733 - 746. 10.35378/gujs.710730
MLA ÖZTÜRK Şaban Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. , 2021, ss.733 - 746. 10.35378/gujs.710730
AMA ÖZTÜRK Ş Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. . 2021; 733 - 746. 10.35378/gujs.710730
Vancouver ÖZTÜRK Ş Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. . 2021; 733 - 746. 10.35378/gujs.710730
IEEE ÖZTÜRK Ş "Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval." , ss.733 - 746, 2021. 10.35378/gujs.710730
ISNAD ÖZTÜRK, Şaban. "Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval". (2021), 733-746. https://doi.org/10.35378/gujs.710730
APA ÖZTÜRK Ş (2021). Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. Gazi University Journal of Science, 34(3), 733 - 746. 10.35378/gujs.710730
Chicago ÖZTÜRK Şaban Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. Gazi University Journal of Science 34, no.3 (2021): 733 - 746. 10.35378/gujs.710730
MLA ÖZTÜRK Şaban Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. Gazi University Journal of Science, vol.34, no.3, 2021, ss.733 - 746. 10.35378/gujs.710730
AMA ÖZTÜRK Ş Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. Gazi University Journal of Science. 2021; 34(3): 733 - 746. 10.35378/gujs.710730
Vancouver ÖZTÜRK Ş Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval. Gazi University Journal of Science. 2021; 34(3): 733 - 746. 10.35378/gujs.710730
IEEE ÖZTÜRK Ş "Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval." Gazi University Journal of Science, 34, ss.733 - 746, 2021. 10.35378/gujs.710730
ISNAD ÖZTÜRK, Şaban. "Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval". Gazi University Journal of Science 34/3 (2021), 733-746. https://doi.org/10.35378/gujs.710730