Akbarzadeh, O, Khosravi, M R, Khosravi, B, Halvaee, P. (1399). Medical Image Magnification Based on Original and Estimated Pixel Selection Models. سامانه مدیریت نشریات علمی, 10(3), 357-366. doi: 10.31661/jbpe.v0i0.797
O Akbarzadeh; M R Khosravi; B Khosravi; P Halvaee. "Medical Image Magnification Based on Original and Estimated Pixel Selection Models". سامانه مدیریت نشریات علمی, 10, 3, 1399, 357-366. doi: 10.31661/jbpe.v0i0.797
Akbarzadeh, O, Khosravi, M R, Khosravi, B, Halvaee, P. (1399). 'Medical Image Magnification Based on Original and Estimated Pixel Selection Models', سامانه مدیریت نشریات علمی, 10(3), pp. 357-366. doi: 10.31661/jbpe.v0i0.797
Akbarzadeh, O, Khosravi, M R, Khosravi, B, Halvaee, P. Medical Image Magnification Based on Original and Estimated Pixel Selection Models. سامانه مدیریت نشریات علمی, 1399; 10(3): 357-366. doi: 10.31661/jbpe.v0i0.797
Medical Image Magnification Based on Original and Estimated Pixel Selection Models
1MSc, Department of Biomedical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran
2MSc, Department of Communications and Electronic Engineering, Shiraz University, Shiraz, Iran
3MSc, Department of Electronics and Telecommunications, Politecnico di Torino, Italy
4PhD, Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran
5PhD, Department of Computer Engineering, Persian Gulf University, Iran
6MSc, Department of Material Science and Engineering, Sharif University of Technology, Tehran, Iran
چکیده
Background: The issue of medial image resolution enhancement is one of the most important topics for medical imaging that helps improve the performance of many post-processing aspects like classification and segmentation towards medical diagnosis. Objective: Our aim in this paper is to evaluate different types of pixel selection models in terms of pixel originality in medical image reconstruction problems. A previous investigation showed that selecting far original pixels has highly better performance than using near unoriginal/estimated pixels while magnifying some benchmarks in digital image processing. Material and Methods: In our technical study, we apply two classical interpolators, cubic convolution (CC) and bi-linear (BL), in order to reconstruct medical images in spatial domain. In addition to the interpolators, we use some geometrical image transforms for creating the reconstruction models. Results: The results clearly demonstrate that despite the absolute preference of the original pixel selection model in the first research, we cannot see this preference in medical dataset in which the results of BL interpolator for both tested models (original and estimated pixel selection models) are approximately the same as each other and for CC interpolator, we only see a relatively better preference for the original pixel selection model. Conclusion: The current research reveals the fact that selection models are not a general factor in reconstruction problems, and the structure of the basic interpolators is also a main factor which affects the final results. In other words, some interpolators in medical dataset can be affected by the selection models, while, some cannot.
Gonzalez RC, Woods RE. Digital image processing. Upper Saddle River, NJ: Prentice Hall; 2012.
Khosravi M, Rostami H. Improving the Binary Watermark-based Data Hiding Scheme in Remote Sensing Images. ICAUCAE; Tehran: SID; 2016.
Khosravi MR, Sharif-Yazd M, Moghimi MK, Keshavarz A, Rostami H, Mansouri S. MRF-based multispectral image fusion using an adaptive approach based on edge-guided interpolation. Journal of Geographic Information System. 2017;9:114-25.
Khosravi M, Keshavarz A, Rostami H, Mansouri S, editors. Statistical Image Fusion for HR Band Colorization in Landsat Sensors. 20th Annual Conference of Computer Society of Iran (CSICC2015); Mashhad: CSI; 2015. p. 245-50.
Unser M, Thevenaz P, Yaroslavsky L. Convolution-based interpolation for fast, high-quality rotation of images. IEEE Trans Image Process. 1995;4:1371-81. doi: 10.1109/83.465102. PubMed PMID: 18291969.
Maeland E. On the comparison of interpolation methods. IEEE Trans Med Imaging. 1988;7:213-7. doi: 10.1109/42.7784. PubMed PMID: 18230471.
Alhihi M. Network Coding for Wireless Sensor Network Cluster over Rayleigh Fading Channel: Finite State Markov Chain. International Journal of Communications, Network and System Sciences. 2017;10:1.
Alhihi M. Practical routing protocol models to improve network performance and adequacy. Journal of Computer and Communications. 2017;5:114-24.
Moghimi MK, Pourghassem H. Shadow Detection Based on Combinations of Hessenberg Decomposition and Principal Component Analysis in Surveillance Applications. IETE Journal of Research. 2015;61:269-84.
Keys R. Cubic convolution interpolation for digital image processing. IEEE Trans Acoust. 1981;29:1153-60.
Hou H, Andrews H. Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust. 1978;26:508-17.
Parker JA, Kenyon RV, Troxel DE. Comparison of interpolating methods for image resampling. IEEE Trans Med Imaging. 1983;2:31-9.
Khosravi M, Rostami H. A New Statistical Technique for Interpolation of Landsat Images. ICAUCAE 2016; Tehran: SID; 2016.
Khosravi MR. Single- and Multi-Step Image Enlargement for Medical Image Coding. Current Signal Transduction Therapy. 2019;14:1-2.
Khosravi MR, Basri H, Khosravi A, Rostami H, editors. Energy efficient spherical divisions for VBF-based routing in dense UWSNs. 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI); Tehran: IEEE; 2015. p. 961-5.
Unser M, Aldroubi A, Eden M. Fast B-spline transforms for continuous image representation and interpolation. IEEE Transactions on pattern analysis and machine intelligence. 1991;13:277-85.
Dodgson NA. Quadratic interpolation for image resampling. IEEE Trans Image Process. 1997;6:1322-6. doi: 10.1109/83.623195. PubMed PMID: 18283021.
Schultz RR, Stevenson RL. A Bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing. 1994;3:233-42.
Grevera GJ, Udupa JK, Miki Y. A task-specific evaluation of three-dimensional image interpolation techniques. IEEE Trans Med Imaging. 1999;18:137-43. doi: 10.1109/42.759116. PubMed PMID: 10232670.
Appledorn CR. A new approach to the interpolation of sampled data. IEEE Trans Med Imaging. 1996;15:369-76. doi: 10.1109/42.500145. PubMed PMID: 18215917.
Lee C, Eden M, Unser M. High-quality image resizing using oblique projection operators. IEEE Trans Image Process. 1998;7:679-92. doi: 10.1109/83.668025. PubMed PMID: 18276284.
Khosravi MR, Khosravi A, Shadloo-Jahromi M, Keshavarz A, editors. A novel fake color scheme based on depth protection for MR passive/optical sensors. 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI); Tehran: EEE; 2015. p. 362-7.
Khosravi MR, Rostami H, Samadi S. Enhancing the Binary Watermark-Based Data Hiding Scheme Using an Interpolation-Based Approach for Optical Remote Sensing Images. International Journal of Agricultural and Environmental Information Systems (IJAEIS). 2018;9:53-71.
