TY - JOUR
AU - 
AB - Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for OPENACCESS stain colour deconvolution of histology images. This approach statistically analyses the Citation: Alsubaie N, Trahearn N, Raza SEA, Snead multi-resolutional representation of the image to separate the independent observations out D, Rajpoot NM (2017) Stain Deconvolution Using of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated Statistical Analysis of Multi-Resolution Stain Colour Representation. PLoS ONE 12(1): e0169875. data. We conducted an extensive set of experiments to compare the proposed method to doi:10.1371/journal.pone.0169875 the recent state of the art methods and demonstrate the robustness of this approach using Editor: Cesario Bianchi, Universidade de Mogi das three different datasets of scanned slides, prepared in different labs using different Cruzes, BRAZIL scanners. Received: August 11, 2016 Accepted: December 23, 2016 Published: January 11, 2017 Copyright:© 2017 Alsubaie et al. This is an open access article distributed under the terms of the Introduction Creative Commons Attribution License, which permits unrestricted use, distribution, and Direct analysis of 
TI - Stain Deconvolution Using Statistical Analysis of Multi-Resolution Stain Colour Representation
JF - PLoS ONE
DO - 10.1371/journal.pone.0169875
DA - 2017-01-11
UR - https://www.deepdyve.com/lp/unpaywall/stain-deconvolution-using-statistical-analysis-of-multi-resolution-1xzqOzFVz1
DP - DeepDyve
ER -