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Image segmentation using snakes and stochastic watershed

with applications to microscopy images of biological tissue

Selig, Bettina (2015). Image segmentation using snakes and stochastic watershed. Diss. (sammanfattning/summary) Uppsala : Sveriges lantbruksuniv., Acta Universitatis agriculturae Sueciae, 1652-6880 ; 2015:16
ISBN 978-91-576-8230-7
eISBN 978-91-576-8231-4
[Doctoral thesis]

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Abstract

The purpose of computerized image analysis is to extract meaningful information from digital images.
To be able to find interesting regions or objects in the image, first, the image needs to be segmented.
This thesis concentrates on two concepts that are used for image segmentation: the snake and the stochastic watershed.

First, we focus on snakes, which are described by contours moving around on the image to find boundaries of objects. Snakes usually fail when concentric contours with similar appearance are supposed to be found successively, because it is impossible for the snake to push off one boundary and settle at the next. This thesis proposes the two-stage snake to overcome this problem. The two-stage snake introduces an intermediate snake that moves away from the influence region of the first boundary, to be able to be attracted by the second boundary. The two-stage snake approach is illustrated on fluorescence microscopy images of compression wood cross-sections for which previously no automated method existed.

Further, we discuss and evolve the idea of stochastic watershed, originally a Monte Carlo approach to determine the most salient contours in the image. This approach has room for improvement concerning runtime and suppression of falsely enhanced boundaries. In this thesis, we propose the exact evaluation of the stochastic watershed (ESW) and the robust stochastic watershed (RSW), which address these two issues separately. With the ESW, we can determine the result without any Monte Carlo simulations, but instead using graph theory. Our algorithm is two orders of magnitude faster than the original approach. The RSW uses noise to disrupt weak boundaries that are consistently found in larger areas. It therefore improves the results for problems where objects differ in size. To benefit from the advantages of both new methods, we merged them in the fast robust stochastic watershed (FRSW). This FRSW uses a few realizations of the ESW, adding noise as in the RSW. Finally, we illustrate the RSW and the FRSW to segment in vivo confocal microscopy images of corneal endothelium. Our methods outperform the automatic segmentation algorithm in the commercial software NAVIS.

Authors/Creators:Selig, Bettina
Title:Image segmentation using snakes and stochastic watershed
Subtitle:with applications to microscopy images of biological tissue
Series/Journal:Acta Universitatis agriculturae Sueciae (1652-6880)
Year of publishing :13 February 2015
Depositing date:13 February 2015
Volume:2015:16
Number of Pages:70
Papers/manuscripts:
NumberReferences
IB. Selig, C. L. Luengo Hendriks, S. Bardage and G. Borgefors, “Segmentation of highly lignified zones in wood fiber cross-sections”, in Image Analysis, ser. Lecture Notes in Computer Science, A.-B. Salberg, J.Y. Hardeberg and R. Jenssen, Eds., vol. 5575, Springer Berlin Heidelberg, pp. 369–378, 2009.
IIB. Selig, C. L. Luengo Hendriks, S. Bardage, G. Daniel and G. Borgefors, “Automatic measurement of compression wood cell attributes in fluorescence microscopy images”, Journal of Microscopy, vol. 246, no. 3, pp. 298–308, 2012.
IIIB. Selig and C. L. Luengo Hendriks, “Stochastic watershed - an analysis”, in Proceedings of SSBA 2012, Swedish Society for Automated Image Analysis, pp. 82–85, 2012.
IVF. Malmberg, B. Selig and C. Luengo Hendriks, “Exact evaluation of stochastic watersheds: from trees to general graphs”, in Discrete Geometry for Computer Imagery, ser. Lecture Notes in Computer Science, E. Barcucci, A. Frosini and S. Rinaldi, Eds., vol. 8668, Springer International Publishing, pp. 309–319, 2014.
VK. B. Bernander, K. Gustavsson, B. Selig, I.-M. Sintorn and C. L. Luengo Hendriks, “Improving the stochastic watershed”, Pattern Recognition Letters, vol. 34, no. 9, pp. 993–1000, 2013.
VIB. Selig, F. Malmberg and C. L. Luengo Hendriks, “The fast evalution of the robust stochstic watershed”, submitted for publication in a conference proceeding, 2015.
VIIB. Selig, K. A. Vermeer, B. Rieger, T. Hillenaar and C. L. Luengo Hendriks, “Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy”, submitted for journal publication, 2015.
Place of Publication:Uppsala
Publisher:Centre for Image Analysis, Swedish University of Agricultural Sciences
ISBN for printed version:978-91-576-8230-7
ISBN for electronic version:978-91-576-8231-4
ISSN:1652-6880
Language:English
Publication Type:Doctoral thesis
Full Text Status:Public
Agris subject categories.:U Auxiliary disciplines > U10 Mathematical and statistical methods
U Auxiliary disciplines > U40 Surveying methods
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > 10201 Computer Science
(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > Computer Vision and Robotics (Autonomous Systems)
(A) Swedish standard research categories 2011 > 3 Medical and Health Sciences > 305 Other Medical Sciences > Other Medical Sciences not elsewhere specified
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Wood Science
Agrovoc terms:image analysis, imagery, methods, eyes, cornea, reaction wood, structures, fluorescence, microscopy
Keywords:image segmentation, snakes, active contours, stochastic watershed, minimal spanning tree, corneal endothelium, compression wood
URN:NBN:urn:nbn:se:slu:epsilon-e-2411
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-e-2411
ID Code:11891
Faculty:S - Faculty of Forest Sciences
Department:(S) > Centre for Image Analysis
Deposited By: Bettina Selig
Deposited On:13 Feb 2015 13:03
Metadata Last Modified:05 Mar 2015 08:55

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