Over the past century, scientific advances in diagnostic devices have offered new potential for noninvasive diagnoses and entrenched computed tomography as a critical component of today's health services. The multidisciplinary field of health image analysis is one of the key areas of innovation that represents these achievements. This area of rapid growth deals with a wide range of operations that support the whole data flow in current health monitoring systems (from raw data capture through digital image transfer). These technologies now have better spatial and luminance resolutions, as well as quicker collection periods, resulting in a large volume of high critical image files that must be appropriately processed and evaluated in order to provide reliable diagnostics findings. This article examines the core kinds of clinical image analysis, as well as the background of various imaging technologies and the major difficulties and developments in the field.
Keywords
Medical Image Processing, Medical Imaging, Positron Emission Tomography, Computed Tomography.
P. Georgiou and C. Toumazou, "CMOS-based programmable gate ISFET", Electronics Letters, vol. 44, no. 22, p. 1289, 2008. Available: 10.1049/el:20082268.
Y. Kurmi and V. Chaurasia, "Multifeature‐based medical image segmentation", IET Image Processing, vol. 12, no. 8, pp. 1491-1498, 2018. Available: 10.1049/iet-ipr.2017.1020.
"Nasopharyngeal Carcinoma (NPC) : The value of 18-Florine Fluorodeoxyglucose (FDG) Positron Emission Tomography Computed Tomography (PET / CT) in comparison to conventional imaging modalities Computed Tomography (CT) and Magnetic Resonance Imaging (MRI)", The Internet Journal of Nuclear Medicine, vol. 5, no. 1, 2009. Available: 10.5580/1294.
M. Rajalakshmi and K. Annapurani, "Enhancement of Vascular Patterns in Palm Images Using Various Image Enhancement Techniques for Person Identification", International Journal of Image and Graphics, p. 2250032, 2021. Available: 10.1142/s0219467822500322.
R. LaLonde, Z. Xu, I. Irmakci, S. Jain and U. Bagci, "Capsules for biomedical image segmentation", Medical Image Analysis, vol. 68, p. 101889, 2021. Available: 10.1016/j.media.2020.101889.
K. Resch and H. Schroeder, "Endoneurosonography: Technique and Equipment, Anatomy and Imaging, and Clinical Application", Operative Neurosurgery, vol. 61, no. 3, pp. ONS-146-ONS-160, 2007. Available: 10.1227/01.neu.0000289728.42954.d5.
L. Zhao, Y. Pan, S. Wang, L. Zhang and M. Islam, "A Hybrid Crack Detection Approach for Scanning Electron Microscope Image Using Deep Learning Method", Scanning, vol. 2021, pp. 1-13, 2021. Available: 10.1155/2021/5558668.
E. McWilliams, A. Yavari and V. Raman, "Aortic root abscess: Multimodality imaging with computed tomography and gallium-67 citrate single-photon emission computed tomography/computed tomography hybrid imaging", Journal of Cardiovascular Computed Tomography, vol. 5, no. 2, pp. 122-124, 2011. Available: 10.1016/j.jcct.2010.10.004.
E. McWilliams, A. Yavari and V. Raman, "Aortic root abscess: Multimodality imaging with computed tomography and gallium-67 citrate single-photon emission computed tomography/computed tomography hybrid imaging", Journal of Cardiovascular Computed Tomography, vol. 5, no. 2, pp. 122-124, 2011. Available: 10.1016/j.jcct.2010.10.004.
T. Naqvi, "Echocardiography in transcatheter aortic valve implantation-Part 1-Transthoracic echocardiography", Echocardiography, vol. 35, no. 7, pp. 1005-1019, 2018. Available: 10.1111/echo.13799.
E. Singh, "Image Enhancement Using Novel Spatial & Frequency Techniques", International Journal Of Engineering And Computer Science, 2016. Available: 10.18535/ijecs/v5i11.55.
A. Haldorai and A. Ramu, “Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability,” Neural Processing Letters, Aug. 2020. doi:10.1007/s11063-020-10327-3
D. Devikanniga, A. Ramu, and A. Haldorai, “Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm,” EAI Endorsed Transactions on Energy Web, p. 164177, Jul. 2018. doi:10.4108/eai.13-7-2018.164177
H. Anandakumar and K. Umamaheswari, “Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers,” Cluster Computing, vol. 20, no. 2, pp. 1505–1515, Mar. 2017.
Acknowledgements
Authors thank Reviewers for taking the time and effort necessary to review the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Mei-Ling
Mei-Ling
School of Engineering and Applied Sciences, Nanjing University, Jiangsu, China.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Mei-Ling, “Scope and Key Areas of Medical Image Processing”, Journal of Biomedical and Sustainable Healthcare Applications, vol.1, no.2, pp. 076-085, July 2021. doi: 10.53759/0088/JBSHA202101010.
