Academic Journal
Design and Development of an Automated Pipeline for Medical Hyperspectral Image Acquisition, Processing, and Fusion.
| Τίτλος: | Design and Development of an Automated Pipeline for Medical Hyperspectral Image Acquisition, Processing, and Fusion. |
|---|---|
| Συγγραφείς: | Wühler, Felix, Häußermann, Tim Markus, Rache, Alessa, van Marwick, Björn, Wängler, Carmen, Reichwald, Julian, Rädle, Matthias |
| Πηγή: | Journal of Imaging; Mar2026, Vol. 12 Issue 3, p99, 21p |
| Θεματικοί όροι: | Multisensor data fusion, Tissue analysis, Hyperspectral imaging systems, Diagnostic imaging, Python programming language, Electronic data processing, Spectrum analysis |
| Περίληψη: | Automated and comprehensive processing of hyperspectral image data is increasingly important in academic research and medical technology. This study presents an automated processing pipeline that integrates hyperspectral image acquisition, analysis, multimodal fusion, and centralized data management to improve the interpretability of spectral information for biological tissue analysis. The pipeline supports modular hyperspectral data processing, fusion of complementary wavelength ranges, and scalable data storage, and was implemented in Python 3.13.3. The pipeline was evaluated using hyperspectral imaging data acquired from a coronal mouse brain section. Clustering-based analysis and spectral correlation metrics were applied to assess the impact of multimodal data fusion on spectral representation. Clustering of individual modalities yielded silhouette coefficients of 0.5879 for near-infrared data, 0.6020 for mid-infrared data, and 0.6715 for RGB data. Multimodal fusion reduced the silhouette coefficient to 0.5420 and enabled the identification of anatomical structures that were not distinguishable in any single modality. High spectral correlation coefficients exceeding 0.98 confirmed that spectral fidelity was preserved during fusion. These results demonstrate that automated multimodal hyperspectral data fusion can enhance the interpretability of biological tissue despite reduced clustering compactness. The proposed pipeline provides a structured framework for preclinical hyperspectral imaging workflows and supports exploratory biological analysis in medical imaging contexts. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Imaging is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=2313433X&ISBN=&volume=12&issue=3&date=20260301&spage=99&pages=99-119&title=Journal of Imaging&atitle=Design%20and%20Development%20of%20an%20Automated%20Pipeline%20for%20Medical%20Hyperspectral%20Image%20Acquisition%2C%20Processing%2C%20and%20Fusion.&aulast=W%C3%BChler%2C%20Felix&id=DOI:10.3390/jimaging12030099 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: edb DbLabel: Complementary Index An: 192625893 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.7568359375 |
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| Items | – Name: Title Label: Title Group: Ti Data: Design and Development of an Automated Pipeline for Medical Hyperspectral Image Acquisition, Processing, and Fusion. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wühler%2C+Felix%22">Wühler, Felix</searchLink><br /><searchLink fieldCode="AR" term="%22Häußermann%2C+Tim+Markus%22">Häußermann, Tim Markus</searchLink><br /><searchLink fieldCode="AR" term="%22Rache%2C+Alessa%22">Rache, Alessa</searchLink><br /><searchLink fieldCode="AR" term="%22van+Marwick%2C+Björn%22">van Marwick, Björn</searchLink><br /><searchLink fieldCode="AR" term="%22Wängler%2C+Carmen%22">Wängler, Carmen</searchLink><br /><searchLink fieldCode="AR" term="%22Reichwald%2C+Julian%22">Reichwald, Julian</searchLink><br /><searchLink fieldCode="AR" term="%22Rädle%2C+Matthias%22">Rädle, Matthias</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Imaging; Mar2026, Vol. 12 Issue 3, p99, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Tissue+analysis%22">Tissue analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Hyperspectral+imaging+systems%22">Hyperspectral imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis%22">Spectrum analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Automated and comprehensive processing of hyperspectral image data is increasingly important in academic research and medical technology. This study presents an automated processing pipeline that integrates hyperspectral image acquisition, analysis, multimodal fusion, and centralized data management to improve the interpretability of spectral information for biological tissue analysis. The pipeline supports modular hyperspectral data processing, fusion of complementary wavelength ranges, and scalable data storage, and was implemented in Python 3.13.3. The pipeline was evaluated using hyperspectral imaging data acquired from a coronal mouse brain section. Clustering-based analysis and spectral correlation metrics were applied to assess the impact of multimodal data fusion on spectral representation. Clustering of individual modalities yielded silhouette coefficients of 0.5879 for near-infrared data, 0.6020 for mid-infrared data, and 0.6715 for RGB data. Multimodal fusion reduced the silhouette coefficient to 0.5420 and enabled the identification of anatomical structures that were not distinguishable in any single modality. High spectral correlation coefficients exceeding 0.98 confirmed that spectral fidelity was preserved during fusion. These results demonstrate that automated multimodal hyperspectral data fusion can enhance the interpretability of biological tissue despite reduced clustering compactness. The proposed pipeline provides a structured framework for preclinical hyperspectral imaging workflows and supports exploratory biological analysis in medical imaging contexts. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Imaging is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/jimaging12030099 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 99 Subjects: – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Tissue analysis Type: general – SubjectFull: Hyperspectral imaging systems Type: general – SubjectFull: Diagnostic imaging Type: general – SubjectFull: Python programming language Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Spectrum analysis Type: general Titles: – TitleFull: Design and Development of an Automated Pipeline for Medical Hyperspectral Image Acquisition, Processing, and Fusion. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wühler, Felix – PersonEntity: Name: NameFull: Häußermann, Tim Markus – PersonEntity: Name: NameFull: Rache, Alessa – PersonEntity: Name: NameFull: van Marwick, Björn – PersonEntity: Name: NameFull: Wängler, Carmen – PersonEntity: Name: NameFull: Reichwald, Julian – PersonEntity: Name: NameFull: Rädle, Matthias IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 2313433X Numbering: – Type: volume Value: 12 – Type: issue Value: 3 Titles: – TitleFull: Journal of Imaging Type: main |
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