Automatic markup of neural cell membranes using boosted decision stumps

Authors:
Kannan Umadevi Venkataraju;Antonio R. C. Paiva;Elizabeth Jurrus;Tolga Tasdizen
Affiliations:
School of Computing, University of Utah and Scientific Computing and Imaging Institute, University of Utah;School of Computing, University of Utah;School of Computing, University of Utah and Scientific Computing and Imaging Institute, University of Utah;Scientific Computing and Imaging Institute, University of Utah and Electrical and Computer Engineering Department, University of Utah
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

To better understand the central nervous system, neurobiologists need to reconstruct the underlying neural circuitry from electron microscopy images. One of the necessary tasks is to segment the individual neurons. For this purpose, we propose a supervised learning approach to detect the cell membranes. The classifier was trained using AdaBoost, on local and context features. The features were selected to highlight the line characteristics of cell membranes. It is shown that using features from context positions allows for more information to be utilized in the classification. Together with the nonlinear discrimination ability of the AdaBoost classifier, this results in clearly noticeable improvements over previously used methods.