Medical Image Magnification Based on Original and Estimated Pixel Selection Models.

Benchmarking Image Compression Image Enhancement Image Interpolation Image Processing, Computer-Assisted Image Reconstruction

Journal

Journal of biomedical physics & engineering
ISSN: 2251-7200
Titre abrégé: J Biomed Phys Eng
Pays: Iran
ID NLM: 101589641

Informations de publication

Date de publication:
Jun 2020
Historique:
received: 18 06 2017
accepted: 10 08 2017
entrez: 9 7 2020
pubmed: 9 7 2020
medline: 9 7 2020
Statut: epublish

Résumé

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. 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. 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. 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. 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.

Sections du résumé

BACKGROUND 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 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 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 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 CONCLUSIONS
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.

Identifiants

DOI: 10.31661/jbpe.v0i0.797 PMID: 32637380 PMC: PMC7321387
pubmed: 32637380
doi: 10.31661/jbpe.v0i0.797
pii: JBPE-10-3
pmc: PMC7321387
doi:

Types de publication

Journal Article

Langues

eng

Pagination

357-366

Informations de copyright

Copyright: © Journal of Biomedical Physics and Engineering.

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Auteurs

Akbarzadeh O (A)

MSc, Department of Biomedical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran.
MSc, Department of Communications and Electronic Engineering, Shiraz University, Shiraz, Iran.
MSc, Department of Electronics and Telecommunications, Politecnico di Torino, Italy.

Khosravi M R (K)

PhD, Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran.
PhD, Department of Computer Engineering, Persian Gulf University, Iran.

Khosravi B (K)

MSc, Department of Material Science and Engineering, Sharif University of Technology, Tehran, Iran.

Halvaee P (H)

MSc, Department of Biomedical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran.

Classifications MeSH