Novel Methods for microglia segmentation, feature extraction and classification
Segmentation and analysis of histological images provides a valuable tool to gain insight into the biology and function of microglial cells in health and disease. Common image segmentation methods are not suitable for inhomogeneous histology image analysis and accurate classification of microglial a...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
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IEEE
2016
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| Online Access: | https://eprints.nottingham.ac.uk/36230/ |
| _version_ | 1848795247938633728 |
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| author | Ding, Yuchun Pardon, Marie-Christine Agostini, Alessandra Faas, Henryk Duan, Jinming Ward, Wil O.C. Easton, Felicity Auer, Dorothee P. Bai, Li |
| author_facet | Ding, Yuchun Pardon, Marie-Christine Agostini, Alessandra Faas, Henryk Duan, Jinming Ward, Wil O.C. Easton, Felicity Auer, Dorothee P. Bai, Li |
| author_sort | Ding, Yuchun |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Segmentation and analysis of histological images provides a valuable tool to gain insight into the biology and function of microglial cells in health and disease. Common image segmentation methods are not suitable for inhomogeneous histology image analysis and accurate classification of microglial activation states has remained a challenge. In this paper, we introduce an automated image analysis framework capable of efficiently segmenting microglial cells from histology images and analysing their morphology. The framework makes use of variational methods and the fast-split Bregman algorithm for image denoising and segmentation, and of multifractal analysis for feature extraction to classify microglia by their activation states. Experiments show that the proposed framework is accurate and scalable to large datasets and provides a useful tool for the study of microglial biology. |
| first_indexed | 2025-11-14T19:29:04Z |
| format | Article |
| id | nottingham-36230 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:29:04Z |
| publishDate | 2016 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-362302020-05-04T17:43:22Z https://eprints.nottingham.ac.uk/36230/ Novel Methods for microglia segmentation, feature extraction and classification Ding, Yuchun Pardon, Marie-Christine Agostini, Alessandra Faas, Henryk Duan, Jinming Ward, Wil O.C. Easton, Felicity Auer, Dorothee P. Bai, Li Segmentation and analysis of histological images provides a valuable tool to gain insight into the biology and function of microglial cells in health and disease. Common image segmentation methods are not suitable for inhomogeneous histology image analysis and accurate classification of microglial activation states has remained a challenge. In this paper, we introduce an automated image analysis framework capable of efficiently segmenting microglial cells from histology images and analysing their morphology. The framework makes use of variational methods and the fast-split Bregman algorithm for image denoising and segmentation, and of multifractal analysis for feature extraction to classify microglia by their activation states. Experiments show that the proposed framework is accurate and scalable to large datasets and provides a useful tool for the study of microglial biology. IEEE 2016-07-14 Article PeerReviewed Ding, Yuchun, Pardon, Marie-Christine, Agostini, Alessandra, Faas, Henryk, Duan, Jinming, Ward, Wil O.C., Easton, Felicity, Auer, Dorothee P. and Bai, Li (2016) Novel Methods for microglia segmentation, feature extraction and classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics (99). ISSN 1557-9964 microglia analysis Mumford-Shah fast split Bregman fast Fourier transform multifractal analysis histology data analysis http://ieeexplore.ieee.org/document/7513440/?arnumber=7513440 doi:10.1109/TCBB.2016.2591520 doi:10.1109/TCBB.2016.2591520 |
| spellingShingle | microglia analysis Mumford-Shah fast split Bregman fast Fourier transform multifractal analysis histology data analysis Ding, Yuchun Pardon, Marie-Christine Agostini, Alessandra Faas, Henryk Duan, Jinming Ward, Wil O.C. Easton, Felicity Auer, Dorothee P. Bai, Li Novel Methods for microglia segmentation, feature extraction and classification |
| title | Novel Methods for microglia segmentation, feature extraction and classification |
| title_full | Novel Methods for microglia segmentation, feature extraction and classification |
| title_fullStr | Novel Methods for microglia segmentation, feature extraction and classification |
| title_full_unstemmed | Novel Methods for microglia segmentation, feature extraction and classification |
| title_short | Novel Methods for microglia segmentation, feature extraction and classification |
| title_sort | novel methods for microglia segmentation, feature extraction and classification |
| topic | microglia analysis Mumford-Shah fast split Bregman fast Fourier transform multifractal analysis histology data analysis |
| url | https://eprints.nottingham.ac.uk/36230/ https://eprints.nottingham.ac.uk/36230/ https://eprints.nottingham.ac.uk/36230/ |