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

Year 2021, , 733 - 746, 01.09.2021

Şaban Öztürk

https://doi.org/10.35378/gujs.710730

Cited By: 15

Abstract

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.

Keywords

Cbmir, Cnn, Retrieval, Hashing

References

• [1] Font MM. Clinical applications of nuclear medicine in the diagnosis and evaluation of musculoskeletal sports injuries. Revista Española de Medicina Nuclear e Imagen Molecular (English Edition). 2020;39:112-34.
• [2] Pang S, Orgun MA, Yu Z. A novel biomedical image indexing and retrieval system via deep preference learning. Computer Methods and Programs in Biomedicine. 2018;158:53-69.
• [3] Alsmadi MK. Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features. Arabian Journal for Science and Engineering. 2020.
• [4] Latif A, Rasheed A, Sajid U, Ahmed J, Ali N, Ratyal NI, et al. Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review. Mathematical Problems in Engineering. 2019;2019:1-21.
• [5] Czajkowska J, Korzekwa S, Pietka E. Computer Aided Diagnosis of Atopic Dermatitis. Computerized Medical Imaging and Graphics. 2020;79.
• [6] Das P, Neelima A. An overview of approaches for content-based medical image retrieval. International Journal of Multimedia Information Retrieval. 2017;6:271-80.
• [7] Jianhua X, Adali T, Yue W. Segmentation of magnetic resonance brain image: integrating region growing and edge detection. Proceedings, International Conference on Image Processing1995. p. 544-7.
• [8] Zhang G, Ma ZM, 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 Processing2008. p. 71-4.
• [9] Chandra Chandra PNRLC, Prasad PS, Kumar MV, Santosh DHH. Image retrieval with rotation invariance. 2011 3rd International Conference on Electronics Computer Technology2011. p. 194-8.
• [10] Jai-Andaloussi S, Lamard M, Cazuguel G, Tairi H, Meknassi M, Cochener B, et al. Content based Medical Image Retrieval: use of Generalized Gaussian Density to model BEMD’s IMF. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany2009. p. 1249-52.
• [11] Ramamurthy B, Chandran KR, Meenakshi VR, Shilpa V. CBMIR: Content Based Medical Image Retrieval System Using Texture and Intensity for Dental Images. Eco-friendly Computing and Communication Systems2012. p. 125-34.
• [12] Babaie M, Tizhoosh HR, Khatami A, Shiri ME. Local radon descriptors for image search. 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)2017. p. 1-5.
• [13] Karakasis EG, Amanatiadis A, Gasteratos A, Chatzichristofis SA. Image moment invariants as local features for content based image retrieval using the Bag-of-Visual-Words model. Pattern Recognition Letters. 2015;55:22-7.
• [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. 2015;154:1-14.
• [15] Banerji S, Sinha A, Liu C. A New Bag of Words LBP (BoWL) Descriptor for Scene Image Classification. Computer Analysis of Images and Patterns2013. p. 490-7.
• [16] Hadjiiski LM, Tourassi GD, Sadek I, Sidibé D, Meriaudeau F. Automatic discrimination of color retinal images using the bag of words approach. Medical Imaging 2015: Computer-Aided Diagnosis2015.
• [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. 2016;8.
• [18] Roy S, Sangineto E, Demir B, Sebe N. Metric-Learning-Based Deep Hashing Network for Content-Based Retrieval of Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. 2020.
• [19] Bressan RS, Alves DHA, Valerio LM, Bugatti PH, Saito PTM. DOCToR: The Role of Deep Features in Content-Based Mammographic Image Retrieval. 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)2018. p. 158-63.
• [20] Owais M, Arsalan M, Choi J, Park KR. Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence. Journal of Clinical Medicine. 2019;8.
• [21] Chen P-H, Bak PR, Krishnamurthi G, Ayyachamy S, Khened M, Alex V. Medical image retrieval using Resnet-18 for clinical diagnosis. Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications2019.
• [22] Cai Y, Li Y, Qiu C, Ma J, Gao X. Medical Image Retrieval Based on Convolutional Neural Network and Supervised Hashing. IEEE Access. 2019;7:51877-85.
• [23] Wang D, Zhao H, Li Q, Kim YH. An image retrieval method of mammary cancer based on convolutional neural network. Journal of Intelligent & Fuzzy Systems. 2020;38:115-26.
• [24] Bootwala A, Breininger K, Maier A, Christlein V. Assistive Diagnosis in Opthalmology Using Deep Learning-Based Image Retrieval. Bildverarbeitung für die Medizin 20202020. p. 144-9.
• [25] Shen S, Sadoughi M, Li M, Wang Z, Hu C. Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries. Applied Energy. 2020;260.
• [26] Khatami A, Babaie M, Tizhoosh HR, Khosravi A, Nguyen T, Nahavandi S. A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval. Expert Systems with Applications. 2018;100:224-33.
• [27] Majhi V, Paul S. Application of Content-Based Image Retrieval in Medical Image Acquisition. Challenges and Applications for Implementing Machine Learning in Computer Vision2020. p. 220-40.
• [28] Wen L, Li X, Gao L. A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Computing and Applications. 2019.
• [29] Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Networks. 2000;13:411-30.
• [30] Zhu Y, Hu X, Zhang Y, Li P. Transfer learning with stacked reconstruction independent component analysis. Knowledge-Based Systems. 2018;152:100-6.
• [31] Sun Y. Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007;29:1035-51.
• [32] Sodhi P, Aggarwal P. Feature Selection Using SEER Data for the Survivability of Ovarian Cancer Patients. Advances in Computing and Intelligent Systems2020. p. 271-9.
• [33] Tang J, Li Z, Zhu X. Supervised deep hashing for scalable face image retrieval. Pattern Recognition. 2018;75:25-32.
• [34] Cao Y, Long M, Liu B, Wang J. Deep Cauchy Hashing for Hamming Space Retrieval. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition2018. p. 1229-37.