Color Normalization by Dictionary Learning with Nuclear Segmentation Evaluation in H&E Histological Images

Cancer is a major health concern in Brazil and globally and is characterized by its high incidence and mortality rates. Diagnosis typically involves the preparation and microscopic analysis of tissue samples, which are often stained with hematoxylin and eosin (H&E). However, color variation in these images poses a significant challenge for computeraided diagnosis systems. This study explored dictionary learning techniques for H&E stain color normalization by utilizing public histological image datasets with varying colors for performance comparisons. The findings revealed that the non-negative matrix factorization techniques outperformed existing methods in the literature, particularly in feature preservation, achieving maximum FSIM, PSNR, QSSIM, and SSIM values of approximately 0.82, 40.21, 0.84, and 0.93, respectively. Furthermore, the impact of normalization on nuclear segmentation highlighted that the visual quality of the normalized images did not directly correlate with the quantitative segmentation results. Therefore, this study raises important open questions for the development of future research in this area.