Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/45507
Title: Quantification and statistical analysis methods for vessel wall components from stained images with Masson's trichrome
Authors: Hernández-Morera, Pablo 
Castaño-González, Irene
Travieso-González, Carlos M. 
Mompeó-Corredera, Blanca
Ortega-Santana, Francisco 
UNESCO Clasification: 32 Ciencias médicas
240401 Bioestadística
Keywords: Masson's Trichrome
Issue Date: 2016
Journal: PLoS ONE 
Abstract: Purpose To develop a digital image processing method to quantify structural components (smooth muscle fibers and extracellular matrix) in the vessel wall stained with Masson’s trichrome, and a statistical method suitable for small sample sizes to analyze the results previously obtained. Methods The quantification method comprises two stages. The pre-processing stage improves tissue image appearance and the vessel wall area is delimited. In the feature extraction stage, the vessel wall components are segmented by grouping pixels with a similar color. The area of each component is calculated by normalizing the number of pixels of each group by the vessel wall area. Statistical analyses are implemented by permutation tests, based on resampling without replacement from the set of the observed data to obtain a sampling distribution of an estimator. The implementation can be parallelized on a multicore machine to reduce execution time. Results The methods have been tested on 48 vessel wall samples of the internal saphenous vein stained with Masson’s trichrome. The results show that the segmented areas are consistent with the perception of a team of doctors and demonstrate good correlation between the expert judgments and the measured parameters for evaluating vessel wall changes. Conclusion The proposed methodology offers a powerful tool to quantify some components of the vessel wall. It is more objective, sensitive and accurate than the biochemical and qualitative methods traditionally used. The permutation tests are suitable statistical techniques to analyze the numerical measurements obtained when the underlying assumptions of the other statistical techniques are not met.
URI: http://hdl.handle.net/10553/45507
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0146954
Source: PLoS ONE [ISSN 1932-6203],v. 11 (1), e0146954
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