Automated processing of digital histopathology slides has the potential to streamline patient care and provide new tools for cancer classification and grading. Before automatic analysis is possible, quality control procedures are applied to ensure that each image can be read consistently. One important quality control step is color normalization of the slide image, which adjusts for color variances (batch-effects) caused by differences in stain preparation and image acquisition equipment. Color batch-effects affect color-based features and reduce the performance of supervised color segmentation algorithms on images acquired separately. To identify an optimal normalization technique for histopathological color segmentation applications, five color normalization algorithms were compared in this study using 204 images from four image batches. Among the normalization methods, two global color normalization methods normalized colors from all stain simultaneously and three stain color normalization methods normalized colors from individual stains extracted using color deconvolution. Stain color normalization methods performed significantly better than global color normalization methods in 11 of 12 cross-batch experiments (p<;0.05). Specifically, the stain color normalization method using k-means clustering was found to be the best choice because of high stain segmentation accuracy and low computational complexity.
[1]
Derek R. Magee,et al.
Colour Normalisation in Digital Histopathology Images
,
2009
.
[2]
Todd H. Stokes,et al.
Pathology imaging informatics for quantitative analysis of whole-slide images
,
2013,
Journal of the American Medical Informatics Association : JAMIA.
[3]
Todd H. Stokes,et al.
Automatic batch-invariant color segmentation of histological cancer images
,
2011,
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[4]
A. Ruifrok,et al.
Quantification of histochemical staining by color deconvolution.
,
2001,
Analytical and quantitative cytology and histology.
[5]
John D. Pfeifer,et al.
Review of the current state of whole slide imaging in pathology
,
2011,
Journal of pathology informatics.
[6]
G. Seber.
Multivariate observations / G.A.F. Seber
,
1983
.
[7]
Hui Kong,et al.
Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting
,
2011,
IEEE Transactions on Medical Imaging.
[8]
Nasser M. Nasrabadi,et al.
Pattern Recognition and Machine Learning
,
2006,
Technometrics.
[9]
Erik Reinhard,et al.
Color Transfer between Images
,
2001,
IEEE Computer Graphics and Applications.