Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections
In order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sectio...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2001-01-01
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| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.1155/2001/683747 |
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| _version_ | 1849402949029068800 |
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| author | Birgitte Nielsen Fritz Albregtsen Wanja Kildal Håvard E. Danielsen |
| author_facet | Birgitte Nielsen Fritz Albregtsen Wanja Kildal Håvard E. Danielsen |
| author_sort | Birgitte Nielsen |
| collection | DOAJ |
| description | In order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sections. Each light microscopy nucleus image was divided into a peripheral and a central part, representing 30% and 70% of the total area of the nucleus, respectively. Textural features were then extracted from the peripheral and central parts of the nuclei images. The adaptive feature extraction was based on Class Difference Matrices and Class Distance Matrices. These matrices were useful to illustrate the difference in chromatin texture between the good and bad prognosis classes of ovarian samples. Class Difference and Distance Matrices also clearly illustrated the difference in texture between the peripheral and central parts of cell nuclei. Both when working with nuclei images from monolayers and from histological sections it seems useful to extract separate features from the peripheral and central parts of the nuclei images. |
| format | Article |
| id | doaj-art-98674c93bf4f4fccb57015a8686ec3a8 |
| institution | Kabale University |
| issn | 0921-8912 1878-3651 |
| language | English |
| publishDate | 2001-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-98674c93bf4f4fccb57015a8686ec3a82025-08-20T03:37:23ZengWileyAnalytical Cellular Pathology0921-89121878-36512001-01-01232758810.1155/2001/683747Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological SectionsBirgitte Nielsen0Fritz Albregtsen1Wanja Kildal2Håvard E. Danielsen3Department of Informatics, University of Oslo, P.O.Box 1080 Blindern, N‐0316 Oslo, NorwayDepartment of Informatics, University of Oslo, P.O.Box 1080 Blindern, N‐0316 Oslo, NorwayDivision of Digital Pathology, The Norwegian Radium Hospital, Montebello, N‐0310 Oslo, NorwayDivision of Digital Pathology, The Norwegian Radium Hospital, Montebello, N‐0310 Oslo, NorwayIn order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sections. Each light microscopy nucleus image was divided into a peripheral and a central part, representing 30% and 70% of the total area of the nucleus, respectively. Textural features were then extracted from the peripheral and central parts of the nuclei images. The adaptive feature extraction was based on Class Difference Matrices and Class Distance Matrices. These matrices were useful to illustrate the difference in chromatin texture between the good and bad prognosis classes of ovarian samples. Class Difference and Distance Matrices also clearly illustrated the difference in texture between the peripheral and central parts of cell nuclei. Both when working with nuclei images from monolayers and from histological sections it seems useful to extract separate features from the peripheral and central parts of the nuclei images.http://dx.doi.org/10.1155/2001/683747 |
| spellingShingle | Birgitte Nielsen Fritz Albregtsen Wanja Kildal Håvard E. Danielsen Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections Analytical Cellular Pathology |
| title | Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections |
| title_full | Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections |
| title_fullStr | Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections |
| title_full_unstemmed | Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections |
| title_short | Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections |
| title_sort | prognostic classification of early ovarian cancer based on very low dimensionality adaptive texture feature vectors from cell nuclei from monolayers and histological sections |
| url | http://dx.doi.org/10.1155/2001/683747 |
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