Multi-spectral in-vivo FPGA-based surgical imaging
dc.check.date | 2023-10-27 | |
dc.check.info | Access to this article is restricted until 12 months after publication by request of the publisher. | en |
dc.contributor.author | Alsharari, Majed | |
dc.contributor.author | Niemitz, Lorenzo | |
dc.contributor.author | Sorensen, Simon | |
dc.contributor.author | Woods, Roger | |
dc.contributor.author | Burke, Ray | |
dc.contributor.author | Andersson Engels, Stefan | |
dc.contributor.author | Reaño, Carlos | |
dc.contributor.author | Mai, Son T. | |
dc.contributor.editor | Gan, Lin | |
dc.contributor.editor | Wang, Yu | |
dc.contributor.editor | Xue, Wei | |
dc.contributor.editor | Chau, Thomas | |
dc.date.accessioned | 2022-12-14T10:19:49Z | |
dc.date.available | 2022-12-14T10:19:49Z | |
dc.date.issued | 2022-10-27 | |
dc.description.abstract | Intelligent 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.status | Peer reviewed | en |
dc.description.version | Accepted Version | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.citation | Alsharari, 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_8 | en |
dc.identifier.doi | 10.1007/978-3-031-19983-7_8 | en |
dc.identifier.endpage | 117 | en |
dc.identifier.issn | 0302-9743 | |
dc.identifier.journaltitle | Lecture Notes in Computer Science | en |
dc.identifier.startpage | 103 | en |
dc.identifier.uri | https://hdl.handle.net/10468/13962 | |
dc.identifier.volume | 13569 | en |
dc.language.iso | en | en |
dc.publisher | Springer Nature Switzerland AG | en |
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_8 | en |
dc.subject | Surgical imaging | en |
dc.subject | Field programmable gate array | en |
dc.title | Multi-spectral in-vivo FPGA-based surgical imaging | en |
dc.type | Article (peer-reviewed) | en |
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