Abstract
The growing volume of medical imaging data presents significant challenges in terms of storage, management, and utilization. Content-based medical image retrieval (CBMIR) systems serve as a supportive tool for radiologists and doctors, enabling healthcare professionals to efficiently compare current cases with previous ones, thereby facilitating accurate and timely diagnoses and treatments. This paper explores various clinically correlated features using the k-nearest neighbors algorithm to retrieve Computed Tomography images of emphysema and with no emphysema based on query content. We examine and compare several combinations of feature extraction techniques, including local binary patterns (LBP), local ternary patterns (LTP), and Zernike moments (ZMs), to evaluate retrieval performance. Our results demonstrate that the integration of LBP, LTP, and ZMs features performs well. However, the retrieval performance of ZMs alone is also quite encouraging compared to the other feature sets.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Figa_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42979-024-03313-2/MediaObjects/42979_2024_3313_Fig9_HTML.png)
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The datasets generated and/or analyzed during the current study are available at https://lauge-soerensen.github.io/emphysema-database/.
References
Thurlbeck WM, Müller NL. Emphysema: definition, imaging, and quantification. AJR Am J Roentgenol. 1994;163(5):1017–25.
Halpin DMG, Criner GJ, Papi A, Singh D, Anzueto A, Martinez FJ, Agusti AA, Vogelmeier CF. Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. The 2020 gold science committee report on covid-19 and chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2021;203(1):24–36.
Ramalingam R, Chinnaiyan V. Intelligent optimization-based pulmonary emphysema detection with adaptive multi-scale dilation assisted residual network with bi-lstm layer. Biomed Signal Process Control. 2024;88: 105643.
Sørensen L, Shaker SB, de Bruijne M. Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans Med Imaging. 2010;29(2):559–69.
Yanase J, Triantaphyllou E. A systematic survey of computer-aided diagnosis in medicine: past and present developments. Expert Syst Appl. 2019;138: 112821.
Shetty R, Bhat VS, Pujari J. Content-based medical image retrieval using deep learning-based features and hybrid meta-heuristic optimization. Biomed Signal Process Control. 2024;92: 106069.
Zhou W, Li H, Tian Q. Recent advance in content-based image retrieval: a literature survey. 2017. arXiv preprint arXiv:1706.06064.
Shikhar Y, Singh VP, Srivastava R. Comparative analysis of distance metrics for designing an effective content-based image retrieval system using colour and texture features. Int J Image Gr Signal Process. 2017;9(12):58.
Singh S, Batra S. An efficient bi-layer content based image retrieval system. Multimedia Tools Appl. 2020;79(25):17731–59.
Vibhav Prakash Singh and Rajeev Srivastava. Content-based mammogram retrieval using wavelet based complete-lbp and k-means clustering for the diagnosis of breast cancer. Int J Hybrid Intell Syst. 2017;14(1–2):31–9.
Kumar A, Kim J, Cai W, Fulham M, Feng D. Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. J Digit Imaging. 2013;26:1025–39.
Babaie M, Kashani H, Kumar MD, Tizhoosh HR . A new local radon descriptor for content-based image search. In: Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25–28, 2020, Proceedings 18. Springer; 2020. pp. 463–472.
Sucharitha G, Senapati RK. Biomedical image retrieval by using local directional edge binary patterns and Zernike moments. Multimedia Tools Appl. 2020;79(3):1847–64.
Aggarwal A, Sharma S, Singh K, Singh H, Kumar S. A new approach for effective retrieval and indexing of medical images. Biomed Signal Process Control. 2019;50:10–34.
Hassan G, Hosny KM, Farouk RM, Alzohairy AM. An efficient retrieval system for biomedical images based on radial associated Laguerre moments. IEEE Access. 2020;8:175669–87.
Wadhera A, Agarwal M. Low dimensional multi-block neighborhood combination pattern for biomedical image retrieval. Multimedia Tools Appl. 2022;81(19):27853–77.
Manikandan T, Maheswari S. Automated classification of emphysema using data augmentation and effective pixel location estimation with multi-scale residual network. Neural Comput Appl. 2022;34(23):20899–914.
Monowar MM, Hamid MA, Ohi AQ, Alassafi MO, Mridha MF. Autoret: a self-supervised spatial recurrent network for content-based image retrieval. Sensors. 2022;22(6):2188.
Pietikäinen M. Local binary patterns Scholarpedia. 2010;5(3):9775.
Srivastava P, Binh NT, Khare A. Content-based image retrieval using moments of local ternary pattern. Mobile Networks Appl. 2014;19:618–25.
Alireza K, Yaw HH. Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell. 1990;12(5):489–97.
Chang R-I, Lin S-Y, Ho J-M, Fann C-W, Wang Y-C. A novel content based image retrieval system using k-means/knn with feature extraction. Comput Sci Inf Syst. 2012;9(4):1645–61.
Author information
Authors and Affiliations
Contributions
AP: Conceptualization, methodology, data collection, analysis, writing—original draft preparation. VPS: Supervision, conceptual guidance, reviewing and editing of the manuscript, and validation of the methodology.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Prakash, A., Singh, V.P. Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features. SN COMPUT. SCI. 5, 950 (2024). https://doi.org/10.1007/s42979-024-03313-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-024-03313-2