A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods
Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before b...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/606857 |
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| _version_ | 1832567351254450176 |
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| author | Cheng Chen John A. Ozolek Wei Wang Gustavo K. Rohde |
| author_facet | Cheng Chen John A. Ozolek Wei Wang Gustavo K. Rohde |
| author_sort | Cheng Chen |
| collection | DOAJ |
| description | Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications. |
| format | Article |
| id | doaj-art-075d9bb9bae94103b59a453ba250c977 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-075d9bb9bae94103b59a453ba250c9772025-02-03T01:01:36ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/606857606857A General System for Automatic Biomedical Image Segmentation Using Intensity NeighborhoodsCheng Chen0John A. Ozolek1Wei Wang2Gustavo K. Rohde3Department of Biomedical Engineering, Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Pathology, Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USADepartment of Biomedical Engineering, Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Biomedical Engineering, Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USAImage segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.http://dx.doi.org/10.1155/2011/606857 |
| spellingShingle | Cheng Chen John A. Ozolek Wei Wang Gustavo K. Rohde A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods International Journal of Biomedical Imaging |
| title | A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods |
| title_full | A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods |
| title_fullStr | A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods |
| title_full_unstemmed | A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods |
| title_short | A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods |
| title_sort | general system for automatic biomedical image segmentation using intensity neighborhoods |
| url | http://dx.doi.org/10.1155/2011/606857 |
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