In-vivo non-contact multispectral oral disease image dataset with segmentation
Abstract In imaging spectroscopy, gathering oral tissue spectral data from resected samples may not accurately represent tissue signatures due to time-dependent changes, blood loss, protein degeneration, and preservation chemicals. In-vivo spectral imaging is employed to address these limitations, b...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04099-x |
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| author | Sneha Chand Karthik Namasivayam Janak Dave S. P. Preejith Sadaksharam Jayachandran Mohanasankar Sivaprakasam |
| author_facet | Sneha Chand Karthik Namasivayam Janak Dave S. P. Preejith Sadaksharam Jayachandran Mohanasankar Sivaprakasam |
| author_sort | Sneha Chand |
| collection | DOAJ |
| description | Abstract In imaging spectroscopy, gathering oral tissue spectral data from resected samples may not accurately represent tissue signatures due to time-dependent changes, blood loss, protein degeneration, and preservation chemicals. In-vivo spectral imaging is employed to address these limitations, but it poses challenges like device dimensions, tissue accessibility, and motion artifacts, impacting data quality and reliability. Our study publishes a dataset of spectral images focusing on oral diseases, addressing these challenges. We used a state-of-the-art multispectral camera, capturing images at 270*510 pixels resolution in 16 spectral bands (460 nm to 600 nm). The dataset includes 91 participants (15 healthy and 76 diseased), with multiple images per patient, totalling 243 spectral images. The dataset encompasses three oral health conditions: Oral Submucous Fibrosis (OSMF), Leukoplakia, and Oral Squamous Cell Carcinoma (OSCC). Detailed patient history records accompany each case. This publicly available oral health multispectral dataset has the potential to advance spectroscopy diagnosis. Integrating artificial intelligence with a comprehensive spectral signature repository holds promise for accurate disease analysis. |
| format | Article |
| id | doaj-art-e017cd17bbde4e60844825032d28cd2d |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-e017cd17bbde4e60844825032d28cd2d2024-12-01T12:09:07ZengNature PortfolioScientific Data2052-44632024-11-011111910.1038/s41597-024-04099-xIn-vivo non-contact multispectral oral disease image dataset with segmentationSneha Chand0Karthik Namasivayam1Janak Dave2S. P. Preejith3Sadaksharam Jayachandran4Mohanasankar Sivaprakasam5Indian Institute of Technology (IIT) Madras, Department of Electrical EngineeringTamil Nadu Government Dental College and Hospital, Department of Oral Medicine and RadiologyHealthcare Technology Innovation Centre (HTIC), Indian Institute of Technology (IIT) MadrasHealthcare Technology Innovation Centre (HTIC), Indian Institute of Technology (IIT) MadrasTamil Nadu Government Dental College and Hospital, Department of Oral Medicine and RadiologyIndian Institute of Technology (IIT) Madras, Department of Electrical EngineeringAbstract In imaging spectroscopy, gathering oral tissue spectral data from resected samples may not accurately represent tissue signatures due to time-dependent changes, blood loss, protein degeneration, and preservation chemicals. In-vivo spectral imaging is employed to address these limitations, but it poses challenges like device dimensions, tissue accessibility, and motion artifacts, impacting data quality and reliability. Our study publishes a dataset of spectral images focusing on oral diseases, addressing these challenges. We used a state-of-the-art multispectral camera, capturing images at 270*510 pixels resolution in 16 spectral bands (460 nm to 600 nm). The dataset includes 91 participants (15 healthy and 76 diseased), with multiple images per patient, totalling 243 spectral images. The dataset encompasses three oral health conditions: Oral Submucous Fibrosis (OSMF), Leukoplakia, and Oral Squamous Cell Carcinoma (OSCC). Detailed patient history records accompany each case. This publicly available oral health multispectral dataset has the potential to advance spectroscopy diagnosis. Integrating artificial intelligence with a comprehensive spectral signature repository holds promise for accurate disease analysis.https://doi.org/10.1038/s41597-024-04099-x |
| spellingShingle | Sneha Chand Karthik Namasivayam Janak Dave S. P. Preejith Sadaksharam Jayachandran Mohanasankar Sivaprakasam In-vivo non-contact multispectral oral disease image dataset with segmentation Scientific Data |
| title | In-vivo non-contact multispectral oral disease image dataset with segmentation |
| title_full | In-vivo non-contact multispectral oral disease image dataset with segmentation |
| title_fullStr | In-vivo non-contact multispectral oral disease image dataset with segmentation |
| title_full_unstemmed | In-vivo non-contact multispectral oral disease image dataset with segmentation |
| title_short | In-vivo non-contact multispectral oral disease image dataset with segmentation |
| title_sort | in vivo non contact multispectral oral disease image dataset with segmentation |
| url | https://doi.org/10.1038/s41597-024-04099-x |
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