Biga, V. and Coca, D. orcid.org/0000-0003-2878-2422 (2017) Information-theoretic active contour model for microscopy image segmentation using texture. In: Bracciali, A., Caravagna, G., Gilbert, D. and Tagliaferri, R., (eds.) Computational Intelligence Methods for Bioinformatics and Biostatistics. 13th International Meeting, CIBB 2016, 01-03 Sep 2016, Stirling, UK. Lecture Notes in Computer Science, 10477 . Springer, Cham , pp. 12-26. ISBN 9783319678337
Abstract
High throughput technologies have increased the need for automated image analysis in a wide variety of microscopy techniques. Geometric active contour models provide a solution to automated image segmentation by incorporating statistical information in the detection of object boundaries. A statistical active contour may be defined by taking into account the optimisation of an information-theoretic measure between object and background. We focus on a product-type measure of divergence known as Cauchy-Schwartz distance which has numerical advantages over ratio-type measures. By using accurate shape derivation techniques, we define a new geometric active contour model for image segmentation combining Cauchy-Schwartz distance and Gabor energy texture filters. We demonstrate the versatility of this approach on images from the Brodatz dataset and phase-contrast microscopy images of cells.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG 2017. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Dec 2017 13:58 |
Last Modified: | 09 Dec 2017 12:59 |
Published Version: | https://doi.org/10.1007/978-3-319-67834-4_2 |
Status: | Published |
Publisher: | Springer, Cham |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-67834-4_2 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124991 |