Application of Improved GS Algorithm In Cell Computing Holography

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An improved Gerchberg-Saxton algorithm reconstructs cell holograms using phase information, yielding better resolution and signal-to-noise ratio than traditional methods, as demonstrated with urine sediment samples.

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The preprint studies improved reconstruction of cell computer holograms by proposing an improved Gerchberg-Saxton (GS) algorithm that uses phase information, and compares reconstructed images obtained under phase-restricted conditions and with added phase mapping and normalization constraints. The key finding is that, across comparative experiments using different models, the improved algorithm outperforms the traditional GS algorithm for cell image reconstruction based on phase information, yielding reconstructions that distinguish cell edge information with higher signal-to-noise ratio. In a urine sediment imaging experiment, the method is reported to filter out impurity cells and produce clearer aberrant red blood cell images with defined edges. The paper is centrally about endometriosis and adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Background: As an important research direction in cell image processing, Computer Hologram(CH) can quantitatively detect and analyze the amplitude and phase information of cells and holographic reconstruct the recorded images. Compared with the traditional optical microscope, CH reduces the complexity of operation, avoids the operation of cell staining and does not affect the physiological characteristics of cells in the recording process. It plays an important role in the field of cell morphology measurement and deformation analysis. Results: An improved Gerchberg-Saxton (GS) algorithm is proposed to reconstruct cell hologram based on phase information. The cell image reconstructed by GS Algorithm with phase restricted conditions are analyzed and compared. Through the comparative experiments of different models, it shows that the improved GS algorithm is better than the traditional GS algorithm in cell image reconstruction based on phase information. With the improved algorithm of phase mapping and normalization conditions, the reconstructed image can distinguish the cell edge information and high signal-to-noise ratio information. The urine sediment image is taken as the experimental object. After reconstruction by this algorithm, other impurity cells can be filtered out, and the aberrant red blood cell image with clear edge can be obtained. This improved algorithm provides a new application for cell detection in clinical diagnosis.Conclusions: The phase map reflects the temporal and spatial information of the image, determines the time or spatial position where different frequency signals appear, and describes the overall shape of the object. Due to biological cells generally for phase type or class phase objects, the improved GS algorithm in this paper can well describe the phase information of restored image, which is more suitable for the practical application of cell phase reconstruction.
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Application of Improved GS Algorithm In Cell Computing Holography | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Methodology Application of Improved GS Algorithm In Cell Computing Holography Xiu xin Wang, Hao wang, Guan Fu, Jiwei Ling This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-720181/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: As an important research direction in cell image processing, Computer Hologram(CH) can quantitatively detect and analyze the amplitude and phase information of cells and holographic reconstruct the recorded images. Compared with the traditional optical microscope, CH reduces the complexity of operation, avoids the operation of cell staining and does not affect the physiological characteristics of cells in the recording process. It plays an important role in the field of cell morphology measurement and deformation analysis. Results: An improved Gerchberg-Saxton (GS) algorithm is proposed to reconstruct cell hologram based on phase information. The cell image reconstructed by GS Algorithm with phase restricted conditions are analyzed and compared. Through the comparative experiments of different models, it shows that the improved GS algorithm is better than the traditional GS algorithm in cell image reconstruction based on phase information. With the improved algorithm of phase mapping and normalization conditions, the reconstructed image can distinguish the cell edge information and high signal-to-noise ratio information. The urine sediment image is taken as the experimental object. After reconstruction by this algorithm, other impurity cells can be filtered out, and the aberrant red blood cell image with clear edge can be obtained. This improved algorithm provides a new application for cell detection in clinical diagnosis. Conclusions: The phase map reflects the temporal and spatial information of the image, determines the time or spatial position where different frequency signals appear, and describes the overall shape of the object. Due to biological cells generally for phase type or class phase objects, the improved GS algorithm in this paper can well describe the phase information of restored image, which is more suitable for the practical application of cell phase reconstruction. Physical Medicine & Rehab Medical Informatics Computer Hologram amplitude Gerchberg-Saxton microscope biological cells Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-720181","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Methodology","associatedPublications":[],"authors":[{"id":42268520,"identity":"f65dd48b-af4a-4aca-b390-f895246c75e2","order_by":0,"name":"Xiu xin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie3LMQuCQBTA8ZMgF3OuIfoKJ4IQRn2VOwJbTmeHhptcXfXbHAi5XLUetLgncdAeaVhTXLo13H949x7cDwCd7n9bvZdRbxK0w6BDSDGAwPJYXCfJeWGLsKxA7GNqHpma8ChYZvzi5CLCFPAdplaElMRjxIMyviAoiEONpMB0akE1OdceRPDUkUcfIohbyZh1hPYgG1F7Rsa3Ts5vOEOHnZtYRE1mKXHvk2S9sMuQSbn356nJ1aRpPH09zAIAteev/00j+SE6nU6n+9YTTKBKjbYqc3kAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2077-3505","institution":"CQUPT: Chongqing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Xiu","middleName":"xin","lastName":"Wang","suffix":""},{"id":42268521,"identity":"8989df78-d637-4487-b6be-f1dc99dce57c","order_by":1,"name":"Hao wang","email":"","orcid":"","institution":"CQUPT: Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"wang","suffix":""},{"id":42268522,"identity":"6982d2c5-2e2f-4d5c-8385-404f9c3fa4f3","order_by":2,"name":"Guan Fu","email":"","orcid":"","institution":"CQUPT: Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Guan","middleName":"","lastName":"Fu","suffix":""},{"id":42268523,"identity":"d6684a23-ddc0-4f22-afc4-88cdf6fc6c79","order_by":3,"name":"Jiwei Ling","email":"","orcid":"","institution":"CQUPT: Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Jiwei","middleName":"","lastName":"Ling","suffix":""}],"badges":[],"createdAt":"2021-07-15 06:29:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-720181/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-720181/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13664012,"identity":"c28416f0-9601-40c4-931d-58e494ca3de0","added_by":"auto","created_at":"2021-09-17 10:39:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1128612,"visible":true,"origin":"","legend":"","description":"","filename":"manuscripts.pdf","url":"https://assets-eu.researchsquare.com/files/rs-720181/v1_covered.pdf"},{"id":11969923,"identity":"16b9a116-441c-4e7e-b39c-1852dc76c4ea","added_by":"auto","created_at":"2021-07-30 19:07:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1125088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscripts.pdf","url":"https://assets-eu.researchsquare.com/files/rs-720181/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eApplication of Improved GS Algorithm In Cell Computing Holography\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-720181/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Computer Hologram, amplitude, Gerchberg-Saxton, microscope, biological cells","lastPublishedDoi":"10.21203/rs.3.rs-720181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-720181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAs an important research direction in cell image processing, Computer Hologram(CH) can quantitatively detect and analyze the amplitude and phase information of cells and holographic reconstruct the recorded images. 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