Journal of Biomedical and Sustainable Healthcare Applications


A Review of Tools Applied in Processing of Medical Images



Journal of Biomedical and Sustainable Healthcare Applications

Received On : 28 August 2020

Revised On : 25 September 2020

Accepted On : 28 October 2020

Published On : 05 January 2021

Volume 01, Issue 01

Pages : 042-049


Abstract


The segmentation step of therapy treatment includes a detailed examination of medical imaging. In diagnosis, clinical research, and patient management, medical pictures are mainly utilized as radiographic methods. Image processing software for medical imaging is also crucial. It is possible to improve and speed up the analysis of a medical picture using a bioMIP technique. This article presents a biomedical imaging software tool that aims to provide a similar level of programmability while investigating pipelined processor solutions. These tools mimic entire systems made up of many of the recommended processing segment within the setups categorized by the schematic framework. In this paper, 15 biomedical imaging technologies will be evaluated on a number of different levels. The comparison's primary goal is to collect and analyze data in order to suggest which medical image program should be used when analyzing various kinds of imaging to users of various operating systems. The article included a result table that was reviewed.


Keywords


Neural Edge Enhancer (NEE), Medical Image Processing (MIP), Signal processing (SP)


  1. W. schOrner, "Picture Archiving and Communication Systems and Videoconference for Medical Communication", Investigative Radiology, vol. 28, pp. S76-78, 1993. Doi: 10.1097/00004424-199308003-00040.
  2. D. Peruzzo, F. Zanderigo, A. Bertoldo, G. Pillonetto, M. Cosottini and C. Cobelli, "Assessment of clinical data of nonlinear stochastic deconvolution versus block-circulant singular value decomposition for quantitative dynamic susceptibility contrast magnetic resonance imaging", Magnetic Resonance Imaging, vol. 29, no. 7, pp. 927-936, 2011. Doi: 10.1016/j.mri.2011.02.006.
  3. K. Suzuki, I. Horiba and N. Sugie, "Neural edge enhancer for supervised edge enhancement from noisy images", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1582-1596, 2003. Doi: 10.1109/tpami.2003.1251151.
  4. Weiyang Zhou, "Verification of the nonparametric characteristics of backpropagation neural networks for image classification", IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 2, pp. 771-779, 1999. Doi: 10.1109/36.752193.
  5. S. D, "Metro Water Fraudulent Prediction in Houses Using Convolutional Neural Network and Recurrent Neural Network", Revista Gestão Inovação e Tecnologias, vol. 11, no. 4, pp. 1177-1187, 2021. Doi: 10.47059/revistageintec.v11i4.2177.
  6. M. Waseem Khan, "A Survey: Image Segmentation Techniques", International Journal of Future Computer and Communication, pp. 89-93, 2014. Doi: 10.7763/ijfcc.2014.v3.274.
  7. T. Cevik, M. Fettahoglu, N. Cevik and S. Yilmaz, "FTSH: a framework for transition from square image processing to hexagonal image processing", Multimedia Tools and Applications, vol. 79, no. 11-12, pp. 7021-7048, 2019. Doi: 10.1007/s11042-019-08487-z.
  8. B. Avants, N. Tustison, G. Song, P. Cook, A. Klein and J. Gee, "A reproducible evaluation of ANTs similarity metric performance in brain image registration", NeuroImage, vol. 54, no. 3, pp. 2033-2044, 2011. Doi: 10.1016/j.neuroimage.2010.09.025.
  9. I. Oguz et al., "Minimally interactive placenta segmentation from three-dimensional ultrasound images", Journal of Medical Imaging, vol. 7, no. 01, p. 1, 2020. Doi: 10.1117/1.jmi.7.1.014004.
  10. G. Wills, "Visualization toolkit software", Wiley Interdisciplinary Reviews: Computational Statistics, vol. 4, no. 5, pp. 474-481, 2012. Doi: 10.1002/wics.1224.
  11. A. Haldorai and A. Ramu, “Security and channel noise management in cognitive radio networks,” Computers & Electrical Engineering, vol. 87, p. 106784, Oct. 2020. doi:10.1016/j.compeleceng.2020.106784
  12. 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
  13. 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
  14. 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.
  15. H. Anandakumar and K. Umamaheswari, “A bio-inspired swarm intelligence technique for social aware cognitive radio handovers,” Computers & Electrical Engineering, vol. 71, pp. 925–937, Oct. 2018. doi:10.1016/j.compeleceng.2017.09.016
  16. R. Arulmurugan and H. Anandakumar, “Early Detection of Lung Cancer Using Wavelet Feature Descriptor and Feed Forward Back Propagation Neural Networks Classifier,” Lecture Notes in Computational Vision and Biomechanics, pp. 103–110, 2018. doi:10.1007/978-3-319-71767-8_9

Acknowledgements


The author(s) received no financial support for the research, authorship, and/or publication of this article.


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


Rights and permissions


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


Anandakumar Haldorai and Shrinand Anandakumar, “A Review of Tools Applied in Processing of Medical Images", vol.1, no.1, pp. 042-049, January 2021. doi: 10.53759/0088/JBSHA202101006.


Copyright


© 2021 Anandakumar Haldorai and Shrinand Anandakumar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.