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. 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

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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.

Item Type: Article
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Keywords: microglia analysis, Mumford-Shah, fast split Bregman, fast Fourier transform, multifractal analysis, histology data analysis
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: Bai, Dr Li
Date Deposited: 05 Sep 2016 10:36
Last Modified: 04 May 2020 17:43

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