Novel Methods for microglia segmentation, feature extraction and classification
Ding, Yuchun and Pardon, Marie-Christine and Agostini, Alessandra and Faas, Henryk and Duan, Jinming and Ward, Wil O.C. and Easton, Felicity and 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
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.
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