Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach
This paper presents an automatic detection method for thin boundaries of silver-stained endothelial cells (ECs) imaged using light microscopy of endothelium mono-layers from rabbit aortas. To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/270247 |
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| _version_ | 1849691155425394688 |
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| author | Saadia Iftikhar Andrew R. Bond Asim I. Wagan Peter D. Weinberg Anil A. Bharath |
| author_facet | Saadia Iftikhar Andrew R. Bond Asim I. Wagan Peter D. Weinberg Anil A. Bharath |
| author_sort | Saadia Iftikhar |
| collection | DOAJ |
| description | This paper presents an automatic detection method for thin boundaries of silver-stained endothelial cells (ECs) imaged using light microscopy of endothelium mono-layers from rabbit aortas. To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the spatial neighbourhood of each pixel and employs a Support Vector Machine (SVM) as a classifier. This segmentation approach is compared, using hand-labelled data, to a number of standard segmentation/thresholding methods commonly applied in microscopy. The importance of different features is also assessed using the method of minimum Redundancy, Maximum Relevance (mRMR), and the effect of different SVM kernels is also considered. The results show that the approach suggested in this paper attains much greater accuracy than standard techniques; in our comparisons with manually labelled data, our proposed technique is able to identify boundary pixels to an accuracy of 93%. More significantly, out of a set of 56 regions of image data, 43 regions were binarised to a useful level of accuracy. The results obtained from the image segmentation technique developed here may be used for the study of shape and alignment of ECs, and hence patterns of blood flow, around arterial branches. |
| format | Article |
| id | doaj-art-e2e0138a75364cc8a759d2ba71fd875d |
| institution | DOAJ |
| 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-e2e0138a75364cc8a759d2ba71fd875d2025-08-20T03:21:07ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/270247270247Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning ApproachSaadia Iftikhar0Andrew R. Bond1Asim I. Wagan2Peter D. Weinberg3Anil A. Bharath4Department of Bioengineering, Imperial College London, London SW7 2AZ, UKDepartment of Bioengineering, Imperial College London, London SW7 2AZ, UKLaboratoire d'InfoRmatique en Image et Systèmes d'information, Institut National des Sciences Appliquées de Lyon, LIRIS INSA De Lyon, 69621 Villeurbanne, FranceDepartment of Bioengineering, Imperial College London, London SW7 2AZ, UKDepartment of Bioengineering, Imperial College London, London SW7 2AZ, UKThis paper presents an automatic detection method for thin boundaries of silver-stained endothelial cells (ECs) imaged using light microscopy of endothelium mono-layers from rabbit aortas. To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the spatial neighbourhood of each pixel and employs a Support Vector Machine (SVM) as a classifier. This segmentation approach is compared, using hand-labelled data, to a number of standard segmentation/thresholding methods commonly applied in microscopy. The importance of different features is also assessed using the method of minimum Redundancy, Maximum Relevance (mRMR), and the effect of different SVM kernels is also considered. The results show that the approach suggested in this paper attains much greater accuracy than standard techniques; in our comparisons with manually labelled data, our proposed technique is able to identify boundary pixels to an accuracy of 93%. More significantly, out of a set of 56 regions of image data, 43 regions were binarised to a useful level of accuracy. The results obtained from the image segmentation technique developed here may be used for the study of shape and alignment of ECs, and hence patterns of blood flow, around arterial branches.http://dx.doi.org/10.1155/2011/270247 |
| spellingShingle | Saadia Iftikhar Andrew R. Bond Asim I. Wagan Peter D. Weinberg Anil A. Bharath Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach International Journal of Biomedical Imaging |
| title | Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach |
| title_full | Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach |
| title_fullStr | Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach |
| title_full_unstemmed | Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach |
| title_short | Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach |
| title_sort | segmentation of endothelial cell boundaries of rabbit aortic images using a machine learning approach |
| url | http://dx.doi.org/10.1155/2011/270247 |
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