Classification of Cells Based on Scale-space Measures and Semi-supervised Machine Learning

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Event Abstract Back to Event Classification of Cells Based on Scale-space Measures and Semi-supervised Machine Learning Sumit K. Vohra1*, Laura Antanas1, Luc De Raedt1 and Dimiter Prodanov2* 1 KU Leuven, Department of Computer Science, Belgium 2 IMEC, Belgium Morphometric analysis and identification of different phenotypes of brain cells has become increasingly important. Accurate identification of individual cellular phenotypes can be obtained by combining automated microscopic acquisition with extensive morphological feature extraction and data mining strategies. In order to classify brain cells in a robust manner involving little human attention, there is a need for effective computational methods for quantification of the neuronal dendritic trees and glial cells. Thus, we propose a semi-supervised active learning-based and geometrical measures for confronting with image's jaggedness. In contrast to threshold based learning approaches, where the learning model is sensitive to global or a local threshold parameters, we evaluated the geometrical features using Laplacian of Gaussian operator (Duits 2003) and scale representation generated by Anisotropic decomposition of the Laplacian (Anisotropic filter, ALoG Filter (Weickert 1998) & (Lindberg 1994)). The learning is performed using active learning framework and Weka. We are implementing a generic tool in ImageJ Plugins for combining all the above computational methods. The technique is successfully applied to two-photon images where intensity threshold-based approaches did not perform adequately (Mols 2013). References Duits R, M. Felsberg, L. Florack, and B. Platel. (2003). Alpha-scale spaces on a bounded domain. Scale Space Methods in Computer Vision, 494- 510. Springer. Lindeberg, T. (1994). Scale-space theory: A basic tool for analysing structures at different scales. J App Stat. 21 (2): 224–270. Weickert J. (1998). Anisotropic Diffusion in Image Processing. ECMI Series. Teubner-Verlag, Stuttgart. Mols K, Prodanov D and Bonin V (2013). Scale-space based segmentation of cells in functional two-photon in vivo images. Front. Neuroinform. Conference Abstract: Imaging the brain at different scales: How to integrate multi-scale structural information?. doi: 10.3389/conf.fninf.2013.10.00033 Keywords: scale-space, machine learning applied to neuroscience, two-photon imaging, Cell segmentation, semi supervised learning Conference: Second Belgian Neuroinformatics Congress, Leuven, Belgium, 4 Dec - 4 Dec, 2015. Presentation Type: Poster Presentation Topic: Methods and Modeling Citation: Vohra SK, Antanas L, De Raedt L and Prodanov D (2015). Classification of Cells Based on Scale-space Measures and Semi-supervised Machine Learning. Front. Neuroinform. Conference Abstract: Second Belgian Neuroinformatics Congress. doi: 10.3389/conf.fninf.2015.19.00020 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Oct 2015; Published Online: 17 Nov 2015. * Correspondence: Mr. Sumit K Vohra, KU Leuven, Department of Computer Science, Leuven, Belgium, sumit.3203@gmail.com Dr. Dimiter Prodanov, IMEC, Leuven, Belgium, dimiterpp@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sumit K Vohra Laura Antanas Luc De Raedt Dimiter Prodanov Google Sumit K Vohra Laura Antanas Luc De Raedt Dimiter Prodanov Google Scholar Sumit K Vohra Laura Antanas Luc De Raedt Dimiter Prodanov PubMed Sumit K Vohra Laura Antanas Luc De Raedt Dimiter Prodanov Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.