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

Abstract

© 2017 IEEE. 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 analyzing 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.

Citation

Ding, Y., Pardon, M. C., Agostini, A., Faas, H., Duan, J., Ward, W. O., …Bai, L. (2016). Novel Methods for Microglia Segmentation, Feature Extraction, and Classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(6), 1366-1377. https://doi.org/10.1109/TCBB.2016.2591520

• Journal Article Type: Article
• Acceptance Date: Jul 10, 2016
• Online Publication Date: Jul 14, 2016
• Publication Date: Jul 14, 2016
• Deposit Date: Sep 5, 2016
• Publicly Available Date: Sep 5, 2016
• Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
• Print ISSN: 1545-5963
• Electronic ISSN: 1557-9964
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 6
• Pages: 1366-1377
• DOI: https://doi.org/10.1109/TCBB.2016.2591520
• Keywords: microglia analysis, Mumford-Shah, fast split Bregman, fast Fourier transform, multifractal analysis, histology; data analysis
• Public URL: https://nottingham-repository.worktribe.com/output/781997
• Publisher URL: http://ieeexplore.ieee.org/document/7513440/?arnumber=7513440
• 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.
• Contract Date: Sep 5, 2016