Automatic selection of parameters for vessel/neurite segmentation algorithms

An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It ena...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 14(2005), 9 vom: 01. Sept., Seite 1338-50
1. Verfasser: Abdul-Karim, Muhammad-Amri (VerfasserIn)
Weitere Verfasser: Roysam, Badrinath, Dowell-Mesfin, Natalie M, Jeromin, Andreas, Yuksel, Murat, Kalyanaraman, Shivkumar
Format: Aufsatz
Sprache:English
Veröffentlicht: 2005
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.
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100 1 |a Abdul-Karim, Muhammad-Amri  |e verfasserin  |4 aut 
245 1 0 |a Automatic selection of parameters for vessel/neurite segmentation algorithms 
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520 |a An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It enables nonexpert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with eight parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7%-21%. Paired t-tests showed that improvements are statistically significant (p < 0.0005). Most of the improvement occurred in the first 44 iterations. Improvements in description lengths and agreement with the ground truth were strongly correlated (p = 0.78) 
650 4 |a Journal Article 
650 4 |a Research Support, N.I.H., Extramural 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
650 4 |a Research Support, U.S. Gov't, P.H.S. 
700 1 |a Roysam, Badrinath  |e verfasserin  |4 aut 
700 1 |a Dowell-Mesfin, Natalie M  |e verfasserin  |4 aut 
700 1 |a Jeromin, Andreas  |e verfasserin  |4 aut 
700 1 |a Yuksel, Murat  |e verfasserin  |4 aut 
700 1 |a Kalyanaraman, Shivkumar  |e verfasserin  |4 aut 
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