Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106644
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dc.contributor.authorAwasthi, Navchetanen
dc.contributor.authorPrabhakar, K. Ramen
dc.contributor.authorKalva, Sandeep Kumaren
dc.contributor.authorPramanik, Manojiten
dc.contributor.authorBabu, R. Venkateshen
dc.contributor.authorYalavarthy, Phaneendra K.en
dc.date.accessioned2019-07-01T06:43:10Zen
dc.date.accessioned2019-12-06T22:15:32Z-
dc.date.available2019-07-01T06:43:10Zen
dc.date.available2019-12-06T22:15:32Z-
dc.date.copyright2019en
dc.date.issued2019en
dc.identifier.citationAwasthi, N., Prabhakar, K. R., Kalva, S. K., Pramanik, M., Babu, R. V., & Yalavarthy, P. K. (2019). PA-Fuse : deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics. Biomedical Optics Express, 10(5), 2227-. doi:10.1364/BOE.10.002227en
dc.identifier.urihttps://hdl.handle.net/10356/106644-
dc.identifier.urihttp://hdl.handle.net/10220/49047en
dc.description.abstractThe methods available for solving the inverse problem of photoacoustic tomography promote only one feature–either being smooth or sharp–in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach. It has the combined benefit of using less data for training without the need for the careful choice of any parameters and is a fully data-driven approach. The proposed deep fusion approach outperformed the contemporary fusion method, which was proved using experimental, numerical phantoms and in-vivo studies. The improvement obtained in the reconstructed images was as high as 95.49% in root mean square error and 7.77 dB in signal to noise ratio (SNR) in comparison to the guided filter approach. Also, it was demonstrated that the proposed deep fuse approach, trained on only blood vessel type images at measurement data SNR being 40 dB, was able to provide a generalization that can work across various noise levels in the measurement data, experimental set-ups as well as imaging objects.en
dc.format.extent17 p.en
dc.language.isoenen
dc.relation.ispartofseriesBiomedical Optics Expressen
dc.rights© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.en
dc.subjectBlood Vesselsen
dc.subjectImage Enhancementen
dc.subjectEngineering::Chemical engineeringen
dc.titlePA-Fuse : deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristicsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Chemical and Biomedical Engineeringen
dc.identifier.doi10.1364/BOE.10.002227en
dc.description.versionPublished versionen
dc.identifier.rims211298en
item.grantfulltextopen-
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