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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2019.2946442
|2 doi
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|a eng
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| 100 |
1 |
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|a Hidalgo-Gavira, Natalia
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Variational Bayesian Blind Color Deconvolution of Histopathological Images
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| 264 |
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|c 2020
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 05.10.2020
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|a Date Revised 05.10.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a Most whole-slide histological images are stained with two or more chemical dyes. Slide stain separation or color deconvolution is a crucial step within the digital pathology workflow. In this paper, the blind color deconvolution problem is formulated within the Bayesian framework. Starting from a multi-stained histological image, our model takes into account both spatial relations among the concentration image pixels and similarity between a given reference color-vector matrix and the estimated one. Using Variational Bayes inference, three efficient new blind color deconvolution methods are proposed which provide automated procedures to estimate all the model parameters in the problem. A comparison with classical and current state-of-the-art color deconvolution algorithms using real images has been carried out demonstrating the superiority of the proposed approach
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Mateos, Javier
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Vega, Miguel
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Molina, Rafael
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Katsaggelos, Aggelos K
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 29(2020), 1 vom: 01., Seite 2026-2036
|w (DE-627)NLM09821456X
|x 1941-0042
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|g volume:29
|g year:2020
|g number:1
|g day:01
|g pages:2026-2036
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|u http://dx.doi.org/10.1109/TIP.2019.2946442
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