Computational Dynamic Features Extraction from Anonymized Medical Images

Authors

  • Angela U. Makolo Department of Computer Science, University of Ibadan, Nigeria Tel: +234 803 862 9948 & +234 703 049 1222
  • Opoola Sunday O Department of Computer Science, University of Ibadan, Nigeria Tel: +234 803 862 9948 & +234 703 049 1222

Keywords:

DICOM, Medical Image, CBIR, Datasets, Texture

Abstract

Images depict clearer meaning than written words and this is reason they are used in a variety of human endeavors, including but not limited to medicine. Medical image datasets are used in medical environment to diagnose and confirm medical disorders for which physical examination may not be sufficient. However, the medical profession's ethics of patient confidentiality policy creates barrier to availability of medical datasets for research; thus, this research work was able to solve the above stated barrier through anonymization of sensitive identity information. Furthermore, the Content Based Image Retrieval (CBIR) using texture as the content was developed to overcome the challenge of information overloading associated with data retrieval systems.

Images acquired from various imaging modalities and placed into Digital Imaging and Communications in Medicine (DICOM) formats were obtained from several hospitals in Nigeria. The database of these images was created and consequently anonymized, then a new anonymized database was created. On anonymized images, feature extraction was done using textures as content and the content was considered for the implementation of retrieval system.

The anonymized images were tested in DICOM view to see if all files were successfully anonymized; the result obtained was 100%. A texture retrieval test was performed, and the number of precisely returned search images using the Similarity Distance Measure formulae resulted in a significant reduction in image overload. Thus, this research work solved the problem of non-availability of datasets for researchers in medical imaging field by providing datasets that can be used without violating law of patient confidentiality by the interested parties. It also solves the problem of hackers obtaining useful information about patients’ datasets. The CBIR using texture as content also enhances and solves the problem of information overloading.

References

. RSNA (2019).Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions. Radiology: Artificial Intelligence 2019; 1(1):e180031 https://doi.org/10.1148/ryai.2019180031

. Burbridge, B. (2020). Dicom Image Anonymization and Transfer to Create a Diagnostic Radiology Teaching File. Int J Radiol Imaging Technol 6:065. doi.org/10.23937/2572-3235.1510065, Volume 6, ISSN: 2572-3235.

. Martin, J. W. et al. (2020). Preparing Medical Imaging Data for Machine Learning. Radiology 2020; 295:4–15, https://doi.org/10.1148/radiol.2020192224

. Trivedi, D.N. et al. (2019). Dental Image Processing for Human Identification. https://doi.org/10.1007/978-3-319-99471-0

. Aishawariya, R.N., Sushmitha, N.S., Nalini, M.K. and Semester, B.E. (2019). Content Based Image Retrieval for the Medical Domain. International Journal of Engineering, Research &Technology (IJERT), ISSN: 2278-0181, Vol. 8, Pg 1345 – 1351

. Feng L. et al, (2017). The Current Role of Image Compression Standards in Medical Imaging, www.mdpi.com/journal/information.

. Anisha T. and Minu C. (2017). K-Anonymization Techniques for hiding multi-sensitive information. Scholars Journal of Engineering and Technology (SJET), Sch, J.Eng. Tech., 2017; 5(6):232-237.

. Sweeney, L. (1998). Datafly: a system for providing anonymity in medical data and Database Security, XI: Status and Prospects, IFIP TC11 WG11.3 11th Int'l Conf. on Database Security, 356-381, 1998.

. Samarati, P. and Sweeney, L. (1998). Generalizing data to provide anonymity when disclosing information. In Proc. of the 17th ACM SIGACTSIGMOD-SIGART. Symposium on Principles of Database Systems (PODS). page 188, Seattle, WA, June 1998.

. Wantong, Z. et’ al (2017). K-Anonymity Algorithm Based on Improved Clustering, Springer International Publishing.

. Alexandros, B., Ioannis, M. and Mihai, L. (2019). PrioPrivacy: A local recoding K-Anonymity Tool for prioritized quasi-identifiers. Association for Computing Machinery, ACM ISBN 978-1-4503-6988-6/19/10.

. Hirata, K. and Kato, T. (1992). Query by visual example – content-based image retrieval. In EDBT’92, Third International Conference on Extending Database Technology, 56-71

. Xiang-Yang, Wan., Bei-Bei, Zhang and Hong-Ying Yang, (2014), Content-based image retrieval by integrating colour and texture Features. Multimed Tools Appl (2014) 68:545–569 DOI 10.1007/s11042-012-1055-7

. HL7. (2008). Website of the Health Level 7 Inc., http://www.hl7.org. Health Level Seven, German usergrouphttps://doi.org/10.1007/978-3-319-99471-0_4

. Priyanka S. (2021). An Overview of Feature Extraction Techniques in Content-Based Image Retrieval Systems. International Journal of Current Engineering and Technology. Vol. 11, No. 1 (Jan./Feb. 2021), P-ISSN 2347-5161.

. Olaleke, et al. (2019). Appraisal of Content Based Image Retrieval (CBIR) Methods, Asian Journal of Research in Computer Science Creative ISBN: 2581-8260.

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Published

2023-02-21

How to Cite

Angela U. Makolo, & Opoola Sunday O. (2023). Computational Dynamic Features Extraction from Anonymized Medical Images. American Scientific Research Journal for Engineering, Technology, and Sciences, 91(1), 151–165. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/8366

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Articles