Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Abstract

This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computerbased classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases -particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns.

Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin

“…This work presents a quantitative evaluation of color deconvolution and color normalization on automated (patch-based) classification of histology images stained with hematoxylin and eosin from breast, prostate, colorectal cancer and malignant lymphoma. The present study extends the preliminary results presented in [ 38 ] and the main contribution is to provide a set of guidelines to select the appropriate combinations color pre-processing/image descriptor for histopathological image analysis. We found that in most cases color pre-processing did not improve classification accuracy, especially when coupled with color-based image descriptors convolutional networks.…”

Pan-cancer image-based detection of clinically actionable genetic alterations

“…Some previous studies have used color normalization for deep learning , while other studies have shown that color normalization can bias histology image classification. 39 The second alternative approach we investigated was to use tiles from the whole slide, as opposed to the tumor region only. In this "weakly supervised" approach, many tiles without invasive cancer tissue were present in the training and inference sets (Suppl.…”

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

“…Color normalization could be appropriate when the images have identical cell or tissue compositions. However, the utilization of color normalization should be handled carefully because it may reduce the accuracy of the classification algorithm related to histopathology images [ 108 ].…”