Enhanced CNN Based Electron Microscopy Image Segmentation

M. Leena Silvoster; V. K. Govindan

doi:10.2478/cait-2012-0014

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Enhanced CNN Based Electron Microscopy Image Segmentation

Cybernetics and Information Technologies

Volume 12 (2012): Issue 2 (June 2012)

By: M. Leena Silvoster and V. K. Govindan

Open Access

|Mar 2013

Abstract

Detecting the neural processes like axons and dendrites needs high quality SEM images. This paper proposes an approach using perceptual grouping via a graph cut and its combinations with Convolutional Neural Network (CNN) to achieve improved segmentation of SEM images. Experimental results demonstrate improved computational efficiency with linear running time.

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Articles in this issue

DOI: https://doi.org/10.2478/cait-2012-0014 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702

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Language: English

Page range: 84 - 97

Published on: Mar 16, 2013

Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Convolutional Neural Network,

SEM image,

perceptual grouping constraints via a graph cut,

segmentation using a graph cut,

graph cut optimization,

affinity graph,

backpropagation,

Maxflow/Mincut algorithm,

Neuronal segmentation

Related subjects:

Computer sciences,

Information technology

© 2013 M. Leena Silvoster, V. K. Govindan, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons License.

Volume 12 (2012): Issue 2 (June 2012)