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DC poleHodnotaJazyk
dc.contributor.authorAlhazmi, Anod
dc.contributor.authorSemwal, Sudhanshu Kumar
dc.contributor.editorSkala, Václav
dc.date.accessioned2020-07-27T08:39:34Z
dc.date.available2020-07-27T08:39:34Z
dc.date.issued2020
dc.identifier.citationWSCG 2020: full papers proceedings: 28th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 29-38.en
dc.identifier.isbn978-80-86943-35-0
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD-ROM)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2020/2020-CSRN-3001.pdf
dc.identifier.urihttp://hdl.handle.net/11025/38447
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2020: full papers proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectrotace invariantnícs
dc.subjectSlicer3Dcs
dc.subjectkruhové balenícs
dc.subjecttransformace vzdálenostics
dc.subjectstereotaktickýcs
dc.titleEpsilon-Rotation Invariance using Approximate Euclidean Spheres Packing Algorithm for Cancer Treatment Planningen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedCancer treatment planning using SRS (Stereotactic Radio Surgery) uses approximate sphere packing algorithms by guiding multiple beams to treat a set of spherical cancerous regions. Usually volume data from CT/MRI scans is used to identify the cancerous region as set of voxels. Computationally optimal Sphere Packing is proven NPComplete. So usually approximate sphere packing algorithms are used to find a set of non-intersecting spheres inside the region of interest (ROI). We implemented a greedy strategy where largest Euclidean spheres are found using distance transformation algorithm. The voxels inside of the largest Euclidean sphere are then subtracted from the ROI, and the next Euclidean sphere is found again from the subtracted volume. The process continues iteratively until we find the desired coverage. In this paper, our goal is to analyze the rotational invariance properties of resulting sphere-packing when the shape of the ROI is rotated. If our sphere packing algorithm generate spheres of identical radius before and after the rotation, then our algorithm could also be used for matching and tracking similar shapes across data sets of multiple patients. In this paper, we describe unique shape descriptors to show that our sphere packing algorithm has high degree of rotation invariance within ±epsilon. We estimate the value of epsilon in the data set for 30 patients by implementing these ideas using Slicer3D™ platform.en
dc.subject.translatedrotation invarianten
dc.subject.translatedSlicer3Den
dc.subject.translatedsphere packingen
dc.subject.translateddistance transformationen
dc.subject.translatedstereotacticen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2020.3001.4
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2020: Full Papers Proceedings

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