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Medical image registration utilizing tissue P systems

The tissue P system (TPS) possesses intrinsic attributes of parallel execution in comprehensive data and instruction space, which provides fast convergence during the transition from local to global optima. Method- In this study, we have proposed and built a TPSysIR framework using the TPS for image...

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Detalles Bibliográficos
Autores principales: Kujur, Saleem Sanatan, Sahana, Sudip Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389265/
https://www.ncbi.nlm.nih.gov/pubmed/35991877
http://dx.doi.org/10.3389/fphar.2022.949872
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author Kujur, Saleem Sanatan
Sahana, Sudip Kumar
author_facet Kujur, Saleem Sanatan
Sahana, Sudip Kumar
author_sort Kujur, Saleem Sanatan
collection PubMed
description The tissue P system (TPS) possesses intrinsic attributes of parallel execution in comprehensive data and instruction space, which provides fast convergence during the transition from local to global optima. Method- In this study, we have proposed and built a TPSysIR framework using the TPS for image registration that optimizes upon the mutual information (MI) similarity metric to find a global solution. Result- The model was tested on single- and multimodal brain MRI scans and other prominent optimization-based image registration techniques. Conclusion- Results show that, among all methods, TPSysIR provides better MI values with minimum deviation in a range of experiment setups conducted iteratively.
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spelling pubmed-93892652022-08-20 Medical image registration utilizing tissue P systems Kujur, Saleem Sanatan Sahana, Sudip Kumar Front Pharmacol Pharmacology The tissue P system (TPS) possesses intrinsic attributes of parallel execution in comprehensive data and instruction space, which provides fast convergence during the transition from local to global optima. Method- In this study, we have proposed and built a TPSysIR framework using the TPS for image registration that optimizes upon the mutual information (MI) similarity metric to find a global solution. Result- The model was tested on single- and multimodal brain MRI scans and other prominent optimization-based image registration techniques. Conclusion- Results show that, among all methods, TPSysIR provides better MI values with minimum deviation in a range of experiment setups conducted iteratively. Frontiers Media S.A. 2022-08-05 /pmc/articles/PMC9389265/ /pubmed/35991877 http://dx.doi.org/10.3389/fphar.2022.949872 Text en Copyright © 2022 Kujur and Sahana. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Kujur, Saleem Sanatan
Sahana, Sudip Kumar
Medical image registration utilizing tissue P systems
title Medical image registration utilizing tissue P systems
title_full Medical image registration utilizing tissue P systems
title_fullStr Medical image registration utilizing tissue P systems
title_full_unstemmed Medical image registration utilizing tissue P systems
title_short Medical image registration utilizing tissue P systems
title_sort medical image registration utilizing tissue p systems
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389265/
https://www.ncbi.nlm.nih.gov/pubmed/35991877
http://dx.doi.org/10.3389/fphar.2022.949872
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