2013
DOI: 10.1186/1746-1596-8-92
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Analyzing huge pathology images with open source software

Abstract: BackgroundDigital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer’s memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and… Show more

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Cited by 102 publications
(64 citation statements)
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“…First, images were opened using NDPI Tools[12] or split into smaller-sized images if opening failed and subsequently converted to 8-bit (grey-scale). Cell density was calculated semi-automatically with the ImageJ plugin ITCN in multiple steps: First, up to 8 representative regions of cells were selected.…”
Section: Methodsmentioning
confidence: 99%
“…First, images were opened using NDPI Tools[12] or split into smaller-sized images if opening failed and subsequently converted to 8-bit (grey-scale). Cell density was calculated semi-automatically with the ImageJ plugin ITCN in multiple steps: First, up to 8 representative regions of cells were selected.…”
Section: Methodsmentioning
confidence: 99%
“…Quantification of elastin immunopositivity was repeated independently at a second centre with the same raw whole‐slide images. Images were split by the use of ndpisplit into tiles of ×5 magnification before the application of a classifier that had been generated by a specialist liver histopathologist using the machine learning weka plugin in fiji . All analysis was undertaken blind to all clinical and histological data.…”
Section: Methodsmentioning
confidence: 99%
“…Files were provided as .ndpi files, which were converted to .tif files using the ndip2tiff software provided by Dr Christophe Deroulers (NDPITools; http://www.imnc.in2p3.fr/pagesperso/deroulers/software/ndpitools/) [22]. The k means clustering algorithm in MATLAB (MATLAB 7.13.0, The MathWorks Inc., Natick, USA http://www.mathworks.com.au/help/stats/kmeans.html) was used to determine exclusive k means clustering of three colours (white, pink and purple) in the haematoxylin and eosin-stained tissue.…”
Section: Methodsmentioning
confidence: 99%