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Automated region of interest retrieval and classification using spectral analysis
Efficient use of whole slide imaging in pathology needs automated region of interest (ROI) retrieval and classification, through the use of image analysis and data sorting tools. One possible method for data sorting uses Spectral Analysis for Dimensionality Reduction. We present some interesting res...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2500116/ https://www.ncbi.nlm.nih.gov/pubmed/18673505 http://dx.doi.org/10.1186/1746-1596-3-S1-S17 |
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author | Oger, Myriam Belhomme, Philippe Klossa, Jacques Michels, Jean-Jacques Elmoataz, Abderrahim |
author_facet | Oger, Myriam Belhomme, Philippe Klossa, Jacques Michels, Jean-Jacques Elmoataz, Abderrahim |
author_sort | Oger, Myriam |
collection | PubMed |
description | Efficient use of whole slide imaging in pathology needs automated region of interest (ROI) retrieval and classification, through the use of image analysis and data sorting tools. One possible method for data sorting uses Spectral Analysis for Dimensionality Reduction. We present some interesting results in the field of histopathology and cytohematology. In histopathology, we developed a Computer-Aided Diagnosis system applied to low-resolution images representing the totality of histological breast tumour sections. The images can be digitized directly at low resolution or be obtained from sub-sampled high-resolution virtual slides. Spectral Analysis is used (1) for image segmentation (stroma, tumour epithelium), by determining a «distance» between all the images of the database, (2) for choosing representative images and characteristic patterns of each histological type in order to index them, and (3) for visualizing images or features similar to a sample provided by the pathologist. In cytohematology, we studied a blood smear virtual slide acquired through high resolution oil scanning and Spectral Analysis is used to sort selected nucleated blood cell classes so that the pathologist may easily focus on specific classes whose morphology could then be studied more carefully or which can be analyzed through complementary instruments, like Multispectral Imaging or Raman MicroSpectroscopy. |
format | Text |
id | pubmed-2500116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-25001162008-08-08 Automated region of interest retrieval and classification using spectral analysis Oger, Myriam Belhomme, Philippe Klossa, Jacques Michels, Jean-Jacques Elmoataz, Abderrahim Diagn Pathol Proceedings Efficient use of whole slide imaging in pathology needs automated region of interest (ROI) retrieval and classification, through the use of image analysis and data sorting tools. One possible method for data sorting uses Spectral Analysis for Dimensionality Reduction. We present some interesting results in the field of histopathology and cytohematology. In histopathology, we developed a Computer-Aided Diagnosis system applied to low-resolution images representing the totality of histological breast tumour sections. The images can be digitized directly at low resolution or be obtained from sub-sampled high-resolution virtual slides. Spectral Analysis is used (1) for image segmentation (stroma, tumour epithelium), by determining a «distance» between all the images of the database, (2) for choosing representative images and characteristic patterns of each histological type in order to index them, and (3) for visualizing images or features similar to a sample provided by the pathologist. In cytohematology, we studied a blood smear virtual slide acquired through high resolution oil scanning and Spectral Analysis is used to sort selected nucleated blood cell classes so that the pathologist may easily focus on specific classes whose morphology could then be studied more carefully or which can be analyzed through complementary instruments, like Multispectral Imaging or Raman MicroSpectroscopy. BioMed Central 2008-07-15 /pmc/articles/PMC2500116/ /pubmed/18673505 http://dx.doi.org/10.1186/1746-1596-3-S1-S17 Text en Copyright © 2008 Oger et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Oger, Myriam Belhomme, Philippe Klossa, Jacques Michels, Jean-Jacques Elmoataz, Abderrahim Automated region of interest retrieval and classification using spectral analysis |
title | Automated region of interest retrieval and classification using spectral analysis |
title_full | Automated region of interest retrieval and classification using spectral analysis |
title_fullStr | Automated region of interest retrieval and classification using spectral analysis |
title_full_unstemmed | Automated region of interest retrieval and classification using spectral analysis |
title_short | Automated region of interest retrieval and classification using spectral analysis |
title_sort | automated region of interest retrieval and classification using spectral analysis |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2500116/ https://www.ncbi.nlm.nih.gov/pubmed/18673505 http://dx.doi.org/10.1186/1746-1596-3-S1-S17 |
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