Automated Classification of Glandular Tissue by Statistical Proximity Sampling
Due to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circum...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2015/943104 |
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| _version_ | 1832554240650772480 |
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| author | Jimmy C. Azar Martin Simonsson Ewert Bengtsson Anders Hast |
| author_facet | Jimmy C. Azar Martin Simonsson Ewert Bengtsson Anders Hast |
| author_sort | Jimmy C. Azar |
| collection | DOAJ |
| description | Due to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances. |
| format | Article |
| id | doaj-art-9d4ab054a5e947c5b2b34dceb781d719 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-9d4ab054a5e947c5b2b34dceb781d7192025-02-03T05:52:08ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962015-01-01201510.1155/2015/943104943104Automated Classification of Glandular Tissue by Statistical Proximity SamplingJimmy C. Azar0Martin Simonsson1Ewert Bengtsson2Anders Hast3Centre for Image Analysis, Department of Information Technology, Uppsala University, 75105 Uppsala, SwedenCentre for Image Analysis, Department of Information Technology, Uppsala University, 75105 Uppsala, SwedenCentre for Image Analysis, Department of Information Technology, Uppsala University, 75105 Uppsala, SwedenCentre for Image Analysis, Department of Information Technology, Uppsala University, 75105 Uppsala, SwedenDue to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances.http://dx.doi.org/10.1155/2015/943104 |
| spellingShingle | Jimmy C. Azar Martin Simonsson Ewert Bengtsson Anders Hast Automated Classification of Glandular Tissue by Statistical Proximity Sampling International Journal of Biomedical Imaging |
| title | Automated Classification of Glandular Tissue by Statistical Proximity Sampling |
| title_full | Automated Classification of Glandular Tissue by Statistical Proximity Sampling |
| title_fullStr | Automated Classification of Glandular Tissue by Statistical Proximity Sampling |
| title_full_unstemmed | Automated Classification of Glandular Tissue by Statistical Proximity Sampling |
| title_short | Automated Classification of Glandular Tissue by Statistical Proximity Sampling |
| title_sort | automated classification of glandular tissue by statistical proximity sampling |
| url | http://dx.doi.org/10.1155/2015/943104 |
| work_keys_str_mv | AT jimmycazar automatedclassificationofglandulartissuebystatisticalproximitysampling AT martinsimonsson automatedclassificationofglandulartissuebystatisticalproximitysampling AT ewertbengtsson automatedclassificationofglandulartissuebystatisticalproximitysampling AT andershast automatedclassificationofglandulartissuebystatisticalproximitysampling |