Tuner : principled parameter finding for image segmentation algorithms using visual response surface exploration

© 2011 IEEE

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 17(2011), 12 vom: 01. Dez., Seite 1892-901
1. Verfasser: Torsney-Weir, Thomas (VerfasserIn)
Weitere Verfasser: Saad, Ahmed, Möller, Torsten, Weber, Britta, Hege, Hans-Christian, Verbavatz, Jean-Marc, Bergner, Steven
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2011
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
LEADER 01000naa a22002652 4500
001 NLM212567470
003 DE-627
005 20231224015803.0
007 cr uuu---uuuuu
008 231224s2011 xx |||||o 00| ||eng c
024 7 |a 10.1109/TVCG.2011.248  |2 doi 
028 5 2 |a pubmed24n0708.xml 
035 |a (DE-627)NLM212567470 
035 |a (NLM)22034306 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Torsney-Weir, Thomas  |e verfasserin  |4 aut 
245 1 0 |a Tuner  |b principled parameter finding for image segmentation algorithms using visual response surface exploration 
264 1 |c 2011 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 24.02.2012 
500 |a Date Revised 25.11.2016 
500 |a published: Print 
500 |a Citation Status MEDLINE 
520 |a © 2011 IEEE 
520 |a In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
700 1 |a Saad, Ahmed  |e verfasserin  |4 aut 
700 1 |a Möller, Torsten  |e verfasserin  |4 aut 
700 1 |a Weber, Britta  |e verfasserin  |4 aut 
700 1 |a Hege, Hans-Christian  |e verfasserin  |4 aut 
700 1 |a Verbavatz, Jean-Marc  |e verfasserin  |4 aut 
700 1 |a Bergner, Steven  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on visualization and computer graphics  |d 1996  |g 17(2011), 12 vom: 01. Dez., Seite 1892-901  |w (DE-627)NLM098269445  |x 1941-0506  |7 nnns 
773 1 8 |g volume:17  |g year:2011  |g number:12  |g day:01  |g month:12  |g pages:1892-901 
856 4 0 |u http://dx.doi.org/10.1109/TVCG.2011.248  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 17  |j 2011  |e 12  |b 01  |c 12  |h 1892-901