Multi-spectral in-vivo FPGA-based surgical imaging

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dc.check.date2023-10-27
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisher.en
dc.contributor.authorAlsharari, Majed
dc.contributor.authorNiemitz, Lorenzo
dc.contributor.authorSorensen, Simon
dc.contributor.authorWoods, Roger
dc.contributor.authorBurke, Ray
dc.contributor.authorAndersson Engels, Stefan
dc.contributor.authorReaño, Carlos
dc.contributor.authorMai, Son T.
dc.contributor.editorGan, Lin
dc.contributor.editorWang, Yu
dc.contributor.editorXue, Wei
dc.contributor.editorChau, Thomas
dc.date.accessioned2022-12-14T10:19:49Z
dc.date.available2022-12-14T10:19:49Z
dc.date.issued2022-10-27
dc.description.abstractIntelligent and adaptive in-vivo, catheter-based imaging systems with enhanced processing and analytical capability have the potential to enhance surgical operations and improve patient care. The paper describes an intelligent surgical imaging system based on a ‘chip on tip’, which reduces the need for conventional imaging. The associated embedded system provides real-time, in-vivo imaging analysis and data display for surgeons, enhancing their ability to detect clinically significant tissue. The paper presents initial work on an field programmable gate array implementation of a contrast limited adaptive histogram equalization algorithm, Hessian matrix construction and region of interest function on the AMD-Xilinx’s Kria KV260 board. It outlines optimizations undertaken to reduce the BRAMs by 38%, DSP48 blocks by 80%, flip-flops by 33% and LUTs by 36%, thus creating a design operating at 121 FPS.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAlsharari, M., Niemitz, L., Sorensen, S., Woods, R., Burke, R., Andersson Engels, S., Reaño, C. and Mai, S. T. (2022) 'Multi-spectral in-vivo FPGA-based surgical imaging', in Gan, L., Wang, Y., Xue, W. and Chau, T. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2022. Lecture Notes in Computer Science, vol. 13569, pp. 103-117. Springer, Cham. https://doi.org/10.1007/978-3-031-19983-7_8en
dc.identifier.doi10.1007/978-3-031-19983-7_8en
dc.identifier.endpage117en
dc.identifier.issn0302-9743
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage103en
dc.identifier.urihttps://hdl.handle.net/10468/13962
dc.identifier.volume13569en
dc.language.isoenen
dc.publisherSpringer Nature Switzerland AGen
dc.rights© 2022, the Authors, under exclusive licence to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of a paper published in Gan, L., Wang, Y., Xue, W. and Chau, T. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2022. Lecture Notes in Computer Science, vol. 13569, pp. 103-117. Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-19983-7_8en
dc.subjectSurgical imagingen
dc.subjectField programmable gate arrayen
dc.titleMulti-spectral in-vivo FPGA-based surgical imagingen
dc.typeArticle (peer-reviewed)en
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